import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy as sp
import seaborn as sns
import itertools
import warnings
warnings.simplefilter("ignore")
import time
data_org=pd.read_csv("Part- 1,2&3 - Signal.csv")
data_org.dtypes
Parameter 1 float64 Parameter 2 float64 Parameter 3 float64 Parameter 4 float64 Parameter 5 float64 Parameter 6 float64 Parameter 7 float64 Parameter 8 float64 Parameter 9 float64 Parameter 10 float64 Parameter 11 float64 Signal_Strength int64 dtype: object
data_org.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Parameter 1 | 1599.0 | 8.319637 | 1.741096 | 4.60000 | 7.1000 | 7.90000 | 9.200000 | 15.90000 |
| Parameter 2 | 1599.0 | 0.527821 | 0.179060 | 0.12000 | 0.3900 | 0.52000 | 0.640000 | 1.58000 |
| Parameter 3 | 1599.0 | 0.270976 | 0.194801 | 0.00000 | 0.0900 | 0.26000 | 0.420000 | 1.00000 |
| Parameter 4 | 1599.0 | 2.538806 | 1.409928 | 0.90000 | 1.9000 | 2.20000 | 2.600000 | 15.50000 |
| Parameter 5 | 1599.0 | 0.087467 | 0.047065 | 0.01200 | 0.0700 | 0.07900 | 0.090000 | 0.61100 |
| Parameter 6 | 1599.0 | 15.874922 | 10.460157 | 1.00000 | 7.0000 | 14.00000 | 21.000000 | 72.00000 |
| Parameter 7 | 1599.0 | 46.467792 | 32.895324 | 6.00000 | 22.0000 | 38.00000 | 62.000000 | 289.00000 |
| Parameter 8 | 1599.0 | 0.996747 | 0.001887 | 0.99007 | 0.9956 | 0.99675 | 0.997835 | 1.00369 |
| Parameter 9 | 1599.0 | 3.311113 | 0.154386 | 2.74000 | 3.2100 | 3.31000 | 3.400000 | 4.01000 |
| Parameter 10 | 1599.0 | 0.658149 | 0.169507 | 0.33000 | 0.5500 | 0.62000 | 0.730000 | 2.00000 |
| Parameter 11 | 1599.0 | 10.422983 | 1.065668 | 8.40000 | 9.5000 | 10.20000 | 11.100000 | 14.90000 |
| Signal_Strength | 1599.0 | 5.636023 | 0.807569 | 3.00000 | 5.0000 | 6.00000 | 6.000000 | 8.00000 |
data_org.shape
(1599, 12)
data=data_org
sns.pairplot(data,hue="Signal_Strength")
<seaborn.axisgrid.PairGrid at 0x2025e555d60>
plt.figure(figsize=(20,12))
sns.heatmap(data.corr())
<AxesSubplot:>
plt.figure(figsize=(20,12))
sns.heatmap(data.cov())
<AxesSubplot:>
for each in data.columns:
#plt.subplot(1,2,1)
sns.displot(data=data[each],kde=True)
plt.show()
#plt.subplot(1,2,2)
sns.boxplot(x=data[each])
plt.show()
plt.close()
data.dtypes
Parameter 1 float64 Parameter 2 float64 Parameter 3 float64 Parameter 4 float64 Parameter 5 float64 Parameter 6 float64 Parameter 7 float64 Parameter 8 float64 Parameter 9 float64 Parameter 10 float64 Parameter 11 float64 Signal_Strength int64 dtype: object
Considering the parameters that are detected as outliers as simply the overdrives of the circuits given so that they are driven into some kind of resonance, we can replace them to understand and substitute for those outliers with the Quartiles.
data_org.describe()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 |
| mean | 8.319637 | 0.527821 | 0.270976 | 2.538806 | 0.087467 | 15.874922 | 46.467792 | 0.996747 | 3.311113 | 0.658149 | 10.422983 | 5.636023 |
| std | 1.741096 | 0.179060 | 0.194801 | 1.409928 | 0.047065 | 10.460157 | 32.895324 | 0.001887 | 0.154386 | 0.169507 | 1.065668 | 0.807569 |
| min | 4.600000 | 0.120000 | 0.000000 | 0.900000 | 0.012000 | 1.000000 | 6.000000 | 0.990070 | 2.740000 | 0.330000 | 8.400000 | 3.000000 |
| 25% | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 5.000000 |
| 50% | 7.900000 | 0.520000 | 0.260000 | 2.200000 | 0.079000 | 14.000000 | 38.000000 | 0.996750 | 3.310000 | 0.620000 | 10.200000 | 6.000000 |
| 75% | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997835 | 3.400000 | 0.730000 | 11.100000 | 6.000000 |
| max | 15.900000 | 1.580000 | 1.000000 | 15.500000 | 0.611000 | 72.000000 | 289.000000 | 1.003690 | 4.010000 | 2.000000 | 14.900000 | 8.000000 |
###############################################################
#
###############################################################
def outlier_elimination(dataframe_given):
dataframe=dataframe_given.copy()
cols=dataframe.columns
#######################################
for col in cols:
data=dataframe[col]
maxpoint=dataframe.describe()[col]["max"]
minpoint=dataframe.describe()[col]["min"]
Q1=dataframe.describe()[col]["25%"]
Q3=dataframe.describe()[col]["75%"]
data[data<Q1]=Q1
data[data>Q3]=Q3
#####################################
return dataframe
###############################################################
#
###############################################################
data=outlier_elimination(data_org.drop("Signal_Strength",axis=1))
data["Signal_Strength"]=data_org["Signal_Strength"]
data.describe()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 |
| mean | 8.077486 | 0.516413 | 0.257455 | 2.227423 | 0.079728 | 13.881176 | 40.362101 | 0.996719 | 3.307223 | 0.633189 | 10.260851 | 5.636023 |
| std | 0.857963 | 0.102133 | 0.133717 | 0.285008 | 0.008013 | 5.641092 | 16.311374 | 0.000902 | 0.077528 | 0.072774 | 0.657560 | 0.807569 |
| min | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 3.000000 |
| 25% | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 5.000000 |
| 50% | 7.900000 | 0.520000 | 0.260000 | 2.200000 | 0.079000 | 14.000000 | 38.000000 | 0.996750 | 3.310000 | 0.620000 | 10.200000 | 6.000000 |
| 75% | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997833 | 3.400000 | 0.730000 | 11.100000 | 6.000000 |
| max | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997835 | 3.400000 | 0.730000 | 11.100000 | 8.000000 |
sns.countplot(x=data["Signal_Strength"])
<AxesSubplot:xlabel='Signal_Strength', ylabel='count'>
data["Signal_Strength"]=data["Signal_Strength"].astype("category")
data.dtypes
Parameter 1 float64 Parameter 2 float64 Parameter 3 float64 Parameter 4 float64 Parameter 5 float64 Parameter 6 float64 Parameter 7 float64 Parameter 8 float64 Parameter 9 float64 Parameter 10 float64 Parameter 11 float64 Signal_Strength category dtype: object
sns.stripplot(data=data,x="Parameter 1",y="Signal_Strength")
<AxesSubplot:xlabel='Parameter 1', ylabel='Signal_Strength'>
for each in data.columns.drop("Signal_Strength"):
grid=sns.FacetGrid(data, col="Signal_Strength")
grid.map(sns.pointplot,each)
#grid.add_legend()
for each in data.columns.drop("Signal_Strength"):
grid=sns.FacetGrid(data, col="Signal_Strength")
grid.map(sns.stripplot,each)
#grid.add_legend()
Looks like there is some particular orientations within the parameters that tend to go more towards right for higher qualtiy signals, on an average, and towards to the left for lower quality signals. The points, along with their mean points seem to be particularly oriented thus. We need to check it out by groupings.
data_groups=data.groupby("Signal_Strength")
data_groups.groups
{3: [459, 517, 690, 832, 899, 1299, 1374, 1469, 1478, 1505], 4: [18, 38, 41, 45, 73, 79, 94, 151, 161, 167, 170, 199, 224, 261, 266, 409, 573, 576, 600, 633, 647, 659, 703, 704, 724, 813, 830, 833, 872, 876, 927, 937, 1124, 1176, 1189, 1233, 1235, 1238, 1239, 1261, 1263, 1276, 1293, 1307, 1363, 1369, 1423, 1461, 1467, 1480, 1482, 1484, 1521], 5: [0, 1, 2, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 23, 25, 26, 27, 28, 30, 32, 34, 39, 40, 43, 44, 46, 47, 48, 49, 50, 53, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 71, 72, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 89, 90, 92, 93, 96, 97, 98, 103, 104, 105, 106, 107, 109, 110, 111, 112, 114, 120, 122, 123, 124, 125, 126, 127, 129, 130, 131, 132, 135, 136, 137, 138, 139, 140, 141, 143, 145, 146, 147, 152, 153, ...], 6: [3, 19, 20, 24, 29, 31, 33, 35, 36, 42, 51, 52, 54, 59, 69, 70, 77, 84, 86, 91, 95, 99, 100, 101, 102, 108, 113, 115, 116, 117, 118, 119, 121, 133, 134, 142, 144, 148, 149, 150, 159, 162, 168, 171, 172, 173, 177, 184, 191, 197, 204, 210, 211, 212, 214, 220, 223, 225, 226, 228, 231, 232, 234, 235, 236, 237, 238, 239, 241, 242, 245, 248, 249, 250, 251, 254, 268, 269, 270, 271, 275, 276, 277, 280, 286, 287, 292, 293, 294, 300, 301, 305, 307, 308, 309, 310, 311, 312, 315, 317, ...], 7: [7, 8, 16, 37, 62, 128, 198, 200, 205, 206, 209, 230, 243, 244, 259, 265, 279, 281, 283, 288, 290, 318, 320, 326, 334, 335, 339, 346, 357, 358, 364, 366, 369, 375, 377, 389, 395, 407, 413, 420, 421, 423, 425, 430, 442, 443, 444, 453, 458, 488, 491, 492, 501, 502, 503, 504, 505, 506, 509, 513, 514, 538, 583, 584, 586, 589, 606, 638, 645, 648, 657, 797, 802, 805, 806, 807, 821, 826, 836, 837, 838, 840, 855, 857, 858, 873, 874, 875, 887, 896, 898, 901, 902, 903, 904, 913, 925, 929, 938, 940, ...], 8: [267, 278, 390, 440, 455, 481, 495, 498, 588, 828, 1061, 1090, 1120, 1202, 1269, 1403, 1449, 1549]}
data_comparison_groups={}
for each in data_groups.groups:
data_to={}
temp=data_groups.get_group(each).describe().T
#print(temp["mean"])
data_to.update({'mean':temp["mean"],'std':temp["std"],'50%':temp["50%"]})
data_comparison_groups.update({each:data_to})
data_comparison=pd.DataFrame(data_comparison_groups)
data_comparison.T
| mean | std | 50% | |
|---|---|---|---|
| 3 | Parameter 1 7.950000 Parameter 2 0.6... | Parameter 1 0.932440 Parameter 2 0.0... | Parameter 1 7.500000 Parameter 2 0.6... |
| 4 | Parameter 1 7.828302 Parameter 2 0.5... | Parameter 1 0.803209 Parameter 2 0.0... | Parameter 1 7.5000 Parameter 2 0.640... |
| 5 | Parameter 1 8.011013 Parameter 2 0.5... | Parameter 1 0.824984 Parameter 2 0.0... | Parameter 1 7.800 Parameter 2 0.580 ... |
| 6 | Parameter 1 8.069906 Parameter 2 0.5... | Parameter 1 0.872088 Parameter 2 0.0... | Parameter 1 7.90000 Parameter 2 0.49... |
| 7 | Parameter 1 8.384925 Parameter 2 0.4... | Parameter 1 0.862211 Parameter 2 0.0... | Parameter 1 8.80000 Parameter 2 0.39... |
| 8 | Parameter 1 8.266667 Parameter 2 0.4... | Parameter 1 0.923548 Parameter 2 0.0... | Parameter 1 8.2500 Parameter 2 0.390... |
data_groups.groups
{3: [459, 517, 690, 832, 899, 1299, 1374, 1469, 1478, 1505], 4: [18, 38, 41, 45, 73, 79, 94, 151, 161, 167, 170, 199, 224, 261, 266, 409, 573, 576, 600, 633, 647, 659, 703, 704, 724, 813, 830, 833, 872, 876, 927, 937, 1124, 1176, 1189, 1233, 1235, 1238, 1239, 1261, 1263, 1276, 1293, 1307, 1363, 1369, 1423, 1461, 1467, 1480, 1482, 1484, 1521], 5: [0, 1, 2, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 23, 25, 26, 27, 28, 30, 32, 34, 39, 40, 43, 44, 46, 47, 48, 49, 50, 53, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 71, 72, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 89, 90, 92, 93, 96, 97, 98, 103, 104, 105, 106, 107, 109, 110, 111, 112, 114, 120, 122, 123, 124, 125, 126, 127, 129, 130, 131, 132, 135, 136, 137, 138, 139, 140, 141, 143, 145, 146, 147, 152, 153, ...], 6: [3, 19, 20, 24, 29, 31, 33, 35, 36, 42, 51, 52, 54, 59, 69, 70, 77, 84, 86, 91, 95, 99, 100, 101, 102, 108, 113, 115, 116, 117, 118, 119, 121, 133, 134, 142, 144, 148, 149, 150, 159, 162, 168, 171, 172, 173, 177, 184, 191, 197, 204, 210, 211, 212, 214, 220, 223, 225, 226, 228, 231, 232, 234, 235, 236, 237, 238, 239, 241, 242, 245, 248, 249, 250, 251, 254, 268, 269, 270, 271, 275, 276, 277, 280, 286, 287, 292, 293, 294, 300, 301, 305, 307, 308, 309, 310, 311, 312, 315, 317, ...], 7: [7, 8, 16, 37, 62, 128, 198, 200, 205, 206, 209, 230, 243, 244, 259, 265, 279, 281, 283, 288, 290, 318, 320, 326, 334, 335, 339, 346, 357, 358, 364, 366, 369, 375, 377, 389, 395, 407, 413, 420, 421, 423, 425, 430, 442, 443, 444, 453, 458, 488, 491, 492, 501, 502, 503, 504, 505, 506, 509, 513, 514, 538, 583, 584, 586, 589, 606, 638, 645, 648, 657, 797, 802, 805, 806, 807, 821, 826, 836, 837, 838, 840, 855, 857, 858, 873, 874, 875, 887, 896, 898, 901, 902, 903, 904, 913, 925, 929, 938, 940, ...], 8: [267, 278, 390, 440, 455, 481, 495, 498, 588, 828, 1061, 1090, 1120, 1202, 1269, 1403, 1449, 1549]}
pd.DataFrame(data_comparison[3]["mean"])
| mean | |
|---|---|
| Parameter 1 | 7.950000 |
| Parameter 2 | 0.611000 |
| Parameter 3 | 0.189000 |
| Parameter 4 | 2.200000 |
| Parameter 5 | 0.083800 |
| Parameter 6 | 10.900000 |
| Parameter 7 | 30.500000 |
| Parameter 8 | 0.997003 |
| Parameter 9 | 3.347000 |
| Parameter 10 | 0.586000 |
| Parameter 11 | 10.115000 |
dataframe_taken={}
for cate in data_comparison.T.columns:
temp=pd.DataFrame()
temp2=pd.DataFrame()
for each in data_groups.groups:
print(cate,each)
print(data_comparison[each][cate])
#print(data_comparison[each][cate])
temp[each]=data_comparison[each][cate]
#temp
#temp2[cate]=data_comparison[cate][each]
#print(data_comparison[each][cate])
print(temp[each])
#print(temp2[cate])
#print(each)
print(" \ntoken end\n")
dataframe_taken.update({cate : temp})
mean 3 Parameter 1 7.950000 Parameter 2 0.611000 Parameter 3 0.189000 Parameter 4 2.200000 Parameter 5 0.083800 Parameter 6 10.900000 Parameter 7 30.500000 Parameter 8 0.997003 Parameter 9 3.347000 Parameter 10 0.586000 Parameter 11 10.115000 Name: mean, dtype: float64 Parameter 1 7.950000 Parameter 2 0.611000 Parameter 3 0.189000 Parameter 4 2.200000 Parameter 5 0.083800 Parameter 6 10.900000 Parameter 7 30.500000 Parameter 8 0.997003 Parameter 9 3.347000 Parameter 10 0.586000 Parameter 11 10.115000 Name: 3, dtype: float64 mean 4 Parameter 1 7.828302 Parameter 2 0.581887 Parameter 3 0.186415 Parameter 4 2.211321 Parameter 5 0.079189 Parameter 6 11.735849 Parameter 7 35.943396 Parameter 8 0.996592 Parameter 9 3.342075 Parameter 10 0.587170 Parameter 11 10.232075 Name: mean, dtype: float64 Parameter 1 7.828302 Parameter 2 0.581887 Parameter 3 0.186415 Parameter 4 2.211321 Parameter 5 0.079189 Parameter 6 11.735849 Parameter 7 35.943396 Parameter 8 0.996592 Parameter 9 3.342075 Parameter 10 0.587170 Parameter 11 10.232075 Name: 4, dtype: float64 mean 5 Parameter 1 8.011013 Parameter 2 0.547283 Parameter 3 0.237269 Parameter 4 2.225330 Parameter 5 0.081222 Parameter 6 14.430250 Parameter 7 44.566814 Parameter 8 0.996895 Parameter 9 3.304258 Parameter 10 0.609354 Parameter 11 9.920485 Name: mean, dtype: float64 Parameter 1 8.011013 Parameter 2 0.547283 Parameter 3 0.237269 Parameter 4 2.225330 Parameter 5 0.081222 Parameter 6 14.430250 Parameter 7 44.566814 Parameter 8 0.996895 Parameter 9 3.304258 Parameter 10 0.609354 Parameter 11 9.920485 Name: 5, dtype: float64 mean 6 Parameter 1 8.069906 Parameter 2 0.500462 Parameter 3 0.260940 Parameter 4 2.216771 Parameter 5 0.079052 Parameter 6 13.973354 Parameter 7 38.750784 Parameter 8 0.996644 Parameter 9 3.311614 Parameter 10 0.644232 Parameter 11 10.420455 Name: mean, dtype: float64 Parameter 1 8.069906 Parameter 2 0.500462 Parameter 3 0.260940 Parameter 4 2.216771 Parameter 5 0.079052 Parameter 6 13.973354 Parameter 7 38.750784 Parameter 8 0.996644 Parameter 9 3.311614 Parameter 10 0.644232 Parameter 11 10.420455 Name: 6, dtype: float64 mean 7 Parameter 1 8.384925 Parameter 2 0.446231 Parameter 3 0.331055 Parameter 4 2.275377 Parameter 5 0.077286 Parameter 6 12.618090 Parameter 7 33.457286 Parameter 8 0.996414 Parameter 9 3.295075 Parameter 10 0.687337 Parameter 11 10.866080 Name: mean, dtype: float64 Parameter 1 8.384925 Parameter 2 0.446231 Parameter 3 0.331055 Parameter 4 2.275377 Parameter 5 0.077286 Parameter 6 12.618090 Parameter 7 33.457286 Parameter 8 0.996414 Parameter 9 3.295075 Parameter 10 0.687337 Parameter 11 10.866080 Name: 7, dtype: float64 mean 8 Parameter 1 8.266667 Parameter 2 0.444444 Parameter 3 0.331111 Parameter 4 2.216667 Parameter 5 0.073500 Parameter 6 11.777778 Parameter 7 33.222222 Parameter 8 0.996298 Parameter 9 3.273333 Parameter 10 0.706667 Parameter 11 10.955556 Name: mean, dtype: float64 Parameter 1 8.266667 Parameter 2 0.444444 Parameter 3 0.331111 Parameter 4 2.216667 Parameter 5 0.073500 Parameter 6 11.777778 Parameter 7 33.222222 Parameter 8 0.996298 Parameter 9 3.273333 Parameter 10 0.706667 Parameter 11 10.955556 Name: 8, dtype: float64 token end std 3 Parameter 1 0.932440 Parameter 2 0.063325 Parameter 3 0.159405 Parameter 4 0.294392 Parameter 5 0.007569 Parameter 6 5.801341 Parameter 7 12.276898 Parameter 8 0.000976 Parameter 9 0.071032 Parameter 10 0.060222 Parameter 11 0.562756 Name: std, dtype: float64 Parameter 1 0.932440 Parameter 2 0.063325 Parameter 3 0.159405 Parameter 4 0.294392 Parameter 5 0.007569 Parameter 6 5.801341 Parameter 7 12.276898 Parameter 8 0.000976 Parameter 9 0.071032 Parameter 10 0.060222 Parameter 11 0.562756 Name: 3, dtype: float64 std 4 Parameter 1 0.803209 Parameter 2 0.086632 Parameter 3 0.126340 Parameter 4 0.283291 Parameter 5 0.008560 Parameter 6 5.360651 Parameter 7 16.300561 Parameter 8 0.000894 Parameter 9 0.066372 Parameter 10 0.056513 Parameter 11 0.667882 Name: std, dtype: float64 Parameter 1 0.803209 Parameter 2 0.086632 Parameter 3 0.126340 Parameter 4 0.283291 Parameter 5 0.008560 Parameter 6 5.360651 Parameter 7 16.300561 Parameter 8 0.000894 Parameter 9 0.066372 Parameter 10 0.056513 Parameter 11 0.667882 Name: 4, dtype: float64 std 5 Parameter 1 0.824984 Parameter 2 0.095784 Parameter 3 0.126095 Parameter 4 0.290032 Parameter 5 0.007699 Parameter 6 5.641878 Parameter 7 16.778541 Parameter 8 0.000835 Parameter 9 0.078013 Parameter 10 0.067485 Parameter 11 0.536666 Name: std, dtype: float64 Parameter 1 0.824984 Parameter 2 0.095784 Parameter 3 0.126095 Parameter 4 0.290032 Parameter 5 0.007699 Parameter 6 5.641878 Parameter 7 16.778541 Parameter 8 0.000835 Parameter 9 0.078013 Parameter 10 0.067485 Parameter 11 0.536666 Name: 5, dtype: float64 std 6 Parameter 1 0.872088 Parameter 2 0.098658 Parameter 3 0.135109 Parameter 4 0.279508 Parameter 5 0.007901 Parameter 6 5.599191 Parameter 7 15.353264 Parameter 8 0.000923 Parameter 9 0.077105 Parameter 10 0.070017 Parameter 11 0.633817 Name: std, dtype: float64 Parameter 1 0.872088 Parameter 2 0.098658 Parameter 3 0.135109 Parameter 4 0.279508 Parameter 5 0.007901 Parameter 6 5.599191 Parameter 7 15.353264 Parameter 8 0.000923 Parameter 9 0.077105 Parameter 10 0.070017 Parameter 11 0.633817 Name: 6, dtype: float64 std 7 Parameter 1 0.862211 Parameter 2 0.086270 Parameter 3 0.123907 Parameter 4 0.280371 Parameter 5 0.008376 Parameter 6 5.469371 Parameter 7 13.875599 Parameter 8 0.000928 Parameter 9 0.075971 Parameter 10 0.061385 Parameter 11 0.412148 Name: std, dtype: float64 Parameter 1 0.862211 Parameter 2 0.086270 Parameter 3 0.123907 Parameter 4 0.280371 Parameter 5 0.008376 Parameter 6 5.469371 Parameter 7 13.875599 Parameter 8 0.000928 Parameter 9 0.075971 Parameter 10 0.061385 Parameter 11 0.412148 Name: 7, dtype: float64 std 8 Parameter 1 0.923548 Parameter 2 0.087058 Parameter 3 0.123378 Parameter 4 0.322217 Parameter 5 0.005056 Parameter 6 5.816362 Parameter 7 15.667710 Parameter 8 0.000908 Parameter 9 0.081746 Parameter 10 0.033255 Parameter 11 0.386876 Name: std, dtype: float64 Parameter 1 0.923548 Parameter 2 0.087058 Parameter 3 0.123378 Parameter 4 0.322217 Parameter 5 0.005056 Parameter 6 5.816362 Parameter 7 15.667710 Parameter 8 0.000908 Parameter 9 0.081746 Parameter 10 0.033255 Parameter 11 0.386876 Name: 8, dtype: float64 token end 50% 3 Parameter 1 7.500000 Parameter 2 0.640000 Parameter 3 0.090000 Parameter 4 2.100000 Parameter 5 0.087000 Parameter 6 7.000000 Parameter 7 22.000000 Parameter 8 0.997443 Parameter 9 3.390000 Parameter 10 0.550000 Parameter 11 9.925000 Name: 50%, dtype: float64 Parameter 1 7.500000 Parameter 2 0.640000 Parameter 3 0.090000 Parameter 4 2.100000 Parameter 5 0.087000 Parameter 6 7.000000 Parameter 7 22.000000 Parameter 8 0.997443 Parameter 9 3.390000 Parameter 10 0.550000 Parameter 11 9.925000 Name: 3, dtype: float64 50% 4 Parameter 1 7.5000 Parameter 2 0.6400 Parameter 3 0.0900 Parameter 4 2.1000 Parameter 5 0.0800 Parameter 6 11.0000 Parameter 7 26.0000 Parameter 8 0.9965 Parameter 9 3.3700 Parameter 10 0.5600 Parameter 11 10.0000 Name: 50%, dtype: float64 Parameter 1 7.5000 Parameter 2 0.6400 Parameter 3 0.0900 Parameter 4 2.1000 Parameter 5 0.0800 Parameter 6 11.0000 Parameter 7 26.0000 Parameter 8 0.9965 Parameter 9 3.3700 Parameter 10 0.5600 Parameter 11 10.0000 Name: 4, dtype: float64 50% 5 Parameter 1 7.800 Parameter 2 0.580 Parameter 3 0.230 Parameter 4 2.200 Parameter 5 0.081 Parameter 6 15.000 Parameter 7 47.000 Parameter 8 0.997 Parameter 9 3.300 Parameter 10 0.580 Parameter 11 9.700 Name: 50%, dtype: float64 Parameter 1 7.800 Parameter 2 0.580 Parameter 3 0.230 Parameter 4 2.200 Parameter 5 0.081 Parameter 6 15.000 Parameter 7 47.000 Parameter 8 0.997 Parameter 9 3.300 Parameter 10 0.580 Parameter 11 9.700 Name: 5, dtype: float64 50% 6 Parameter 1 7.90000 Parameter 2 0.49000 Parameter 3 0.26000 Parameter 4 2.20000 Parameter 5 0.07800 Parameter 6 14.00000 Parameter 7 35.00000 Parameter 8 0.99656 Parameter 9 3.32000 Parameter 10 0.64000 Parameter 11 10.50000 Name: 50%, dtype: float64 Parameter 1 7.90000 Parameter 2 0.49000 Parameter 3 0.26000 Parameter 4 2.20000 Parameter 5 0.07800 Parameter 6 14.00000 Parameter 7 35.00000 Parameter 8 0.99656 Parameter 9 3.32000 Parameter 10 0.64000 Parameter 11 10.50000 Name: 6, dtype: float64 50% 7 Parameter 1 8.80000 Parameter 2 0.39000 Parameter 3 0.40000 Parameter 4 2.30000 Parameter 5 0.07300 Parameter 6 11.00000 Parameter 7 27.00000 Parameter 8 0.99577 Parameter 9 3.28000 Parameter 10 0.73000 Parameter 11 11.10000 Name: 50%, dtype: float64 Parameter 1 8.80000 Parameter 2 0.39000 Parameter 3 0.40000 Parameter 4 2.30000 Parameter 5 0.07300 Parameter 6 11.00000 Parameter 7 27.00000 Parameter 8 0.99577 Parameter 9 3.28000 Parameter 10 0.73000 Parameter 11 11.10000 Name: 7, dtype: float64 50% 8 Parameter 1 8.2500 Parameter 2 0.3900 Parameter 3 0.4050 Parameter 4 2.1000 Parameter 5 0.0705 Parameter 6 7.5000 Parameter 7 23.0000 Parameter 8 0.9956 Parameter 9 3.2300 Parameter 10 0.7300 Parameter 11 11.1000 Name: 50%, dtype: float64 Parameter 1 8.2500 Parameter 2 0.3900 Parameter 3 0.4050 Parameter 4 2.1000 Parameter 5 0.0705 Parameter 6 7.5000 Parameter 7 23.0000 Parameter 8 0.9956 Parameter 9 3.2300 Parameter 10 0.7300 Parameter 11 11.1000 Name: 8, dtype: float64 token end
dataframe_taken["std"] #Standard Deviations for varios parameters arranged according to the mean of the corresponding signal strength
| 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|
| Parameter 1 | 0.932440 | 0.803209 | 0.824984 | 0.872088 | 0.862211 | 0.923548 |
| Parameter 2 | 0.063325 | 0.086632 | 0.095784 | 0.098658 | 0.086270 | 0.087058 |
| Parameter 3 | 0.159405 | 0.126340 | 0.126095 | 0.135109 | 0.123907 | 0.123378 |
| Parameter 4 | 0.294392 | 0.283291 | 0.290032 | 0.279508 | 0.280371 | 0.322217 |
| Parameter 5 | 0.007569 | 0.008560 | 0.007699 | 0.007901 | 0.008376 | 0.005056 |
| Parameter 6 | 5.801341 | 5.360651 | 5.641878 | 5.599191 | 5.469371 | 5.816362 |
| Parameter 7 | 12.276898 | 16.300561 | 16.778541 | 15.353264 | 13.875599 | 15.667710 |
| Parameter 8 | 0.000976 | 0.000894 | 0.000835 | 0.000923 | 0.000928 | 0.000908 |
| Parameter 9 | 0.071032 | 0.066372 | 0.078013 | 0.077105 | 0.075971 | 0.081746 |
| Parameter 10 | 0.060222 | 0.056513 | 0.067485 | 0.070017 | 0.061385 | 0.033255 |
| Parameter 11 | 0.562756 | 0.667882 | 0.536666 | 0.633817 | 0.412148 | 0.386876 |
dataframe_taken["std"].T
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 0.932440 | 0.063325 | 0.159405 | 0.294392 | 0.007569 | 5.801341 | 12.276898 | 0.000976 | 0.071032 | 0.060222 | 0.562756 |
| 4 | 0.803209 | 0.086632 | 0.126340 | 0.283291 | 0.008560 | 5.360651 | 16.300561 | 0.000894 | 0.066372 | 0.056513 | 0.667882 |
| 5 | 0.824984 | 0.095784 | 0.126095 | 0.290032 | 0.007699 | 5.641878 | 16.778541 | 0.000835 | 0.078013 | 0.067485 | 0.536666 |
| 6 | 0.872088 | 0.098658 | 0.135109 | 0.279508 | 0.007901 | 5.599191 | 15.353264 | 0.000923 | 0.077105 | 0.070017 | 0.633817 |
| 7 | 0.862211 | 0.086270 | 0.123907 | 0.280371 | 0.008376 | 5.469371 | 13.875599 | 0.000928 | 0.075971 | 0.061385 | 0.412148 |
| 8 | 0.923548 | 0.087058 | 0.123378 | 0.322217 | 0.005056 | 5.816362 | 15.667710 | 0.000908 | 0.081746 | 0.033255 | 0.386876 |
dataframe_taken["mean"].T
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 7.950000 | 0.611000 | 0.189000 | 2.200000 | 0.083800 | 10.900000 | 30.500000 | 0.997003 | 3.347000 | 0.586000 | 10.115000 |
| 4 | 7.828302 | 0.581887 | 0.186415 | 2.211321 | 0.079189 | 11.735849 | 35.943396 | 0.996592 | 3.342075 | 0.587170 | 10.232075 |
| 5 | 8.011013 | 0.547283 | 0.237269 | 2.225330 | 0.081222 | 14.430250 | 44.566814 | 0.996895 | 3.304258 | 0.609354 | 9.920485 |
| 6 | 8.069906 | 0.500462 | 0.260940 | 2.216771 | 0.079052 | 13.973354 | 38.750784 | 0.996644 | 3.311614 | 0.644232 | 10.420455 |
| 7 | 8.384925 | 0.446231 | 0.331055 | 2.275377 | 0.077286 | 12.618090 | 33.457286 | 0.996414 | 3.295075 | 0.687337 | 10.866080 |
| 8 | 8.266667 | 0.444444 | 0.331111 | 2.216667 | 0.073500 | 11.777778 | 33.222222 | 0.996298 | 3.273333 | 0.706667 | 10.955556 |
dataframe_taken["50%"].T
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 7.50 | 0.64 | 0.090 | 2.1 | 0.0870 | 7.0 | 22.0 | 0.997443 | 3.39 | 0.55 | 9.925 |
| 4 | 7.50 | 0.64 | 0.090 | 2.1 | 0.0800 | 11.0 | 26.0 | 0.996500 | 3.37 | 0.56 | 10.000 |
| 5 | 7.80 | 0.58 | 0.230 | 2.2 | 0.0810 | 15.0 | 47.0 | 0.997000 | 3.30 | 0.58 | 9.700 |
| 6 | 7.90 | 0.49 | 0.260 | 2.2 | 0.0780 | 14.0 | 35.0 | 0.996560 | 3.32 | 0.64 | 10.500 |
| 7 | 8.80 | 0.39 | 0.400 | 2.3 | 0.0730 | 11.0 | 27.0 | 0.995770 | 3.28 | 0.73 | 11.100 |
| 8 | 8.25 | 0.39 | 0.405 | 2.1 | 0.0705 | 7.5 | 23.0 | 0.995600 | 3.23 | 0.73 | 11.100 |
plt.figure(figsize=(20,12))
plt.title("Median distribution for Parameters as per the Signal Quality")
plt.xlabel("Signal_Strength")
plt.ylabel("Median")
plt.plot(dataframe_taken["50%"].T)
plt.legend(dataframe_taken["50%"].T.columns)
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>
plt.figure(figsize=(20,12))
plt.title("Mean distribution for Parameters as per the Signal Quality")
plt.xlabel("Signal_Strength")
plt.ylabel("Mean")
plt.plot(dataframe_taken["mean"].T)
plt.legend(dataframe_taken["mean"].T.columns)
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>
plt.figure(figsize=(20,12))
plt.title("Standard Deviation distribution for Parameters as per the Signal Quality")
plt.xlabel("Signal_Strength")
plt.ylabel("Standard Deviation")
plt.plot(dataframe_taken["std"].T)
plt.legend(dataframe_taken["std"].T.columns)
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>
data2=data.drop(["Signal_Strength"],axis=1)
data2.corr()[(data2.corr()>0.6) | (data2.corr()<-0.6)]
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Parameter 1 | 1.000000 | NaN | 0.637751 | NaN | NaN | NaN | NaN | NaN | -0.655172 | NaN | NaN |
| Parameter 2 | NaN | 1.000000 | -0.639238 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Parameter 3 | 0.637751 | -0.639238 | 1.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Parameter 4 | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Parameter 5 | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN |
| Parameter 6 | NaN | NaN | NaN | NaN | NaN | 1.000000 | 0.751993 | NaN | NaN | NaN | NaN |
| Parameter 7 | NaN | NaN | NaN | NaN | NaN | 0.751993 | 1.000000 | NaN | NaN | NaN | NaN |
| Parameter 8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN |
| Parameter 9 | -0.655172 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | NaN | NaN |
| Parameter 10 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN |
| Parameter 11 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 |
Parameter 2 has no correlation with any of the other parameters. So, it looks like a real independent and non-redundant column and hence of crucial and very important. But the distribution plot of the parameters indicates otherwise stating that most probably the parameter doesn't play any role at all in determining whether the Signal has proper strength or not.
The same Story aplies for Parameter 4 and Parameter 5. Scatterplots speak the same in this case too.
Most of the action seems to be happening with Parameter 1, in connection with Parameter8, Parameter8, Parameter9.
parameter_interest1=["Parameter 1", "Parameter 3", "Parameter 6", "Parameter 7", "Parameter 8", "Parameter 9"]
parameter_other=data2.columns.drop(parameter_interest1)
parameter_other
Index(['Parameter 2', 'Parameter 4', 'Parameter 5', 'Parameter 10',
'Parameter 11'],
dtype='object')
for each1,each2 in itertools.combinations(parameter_interest1,2):
sns.regplot(x=data[each1],y=data[each2])
plt.show()
plt.close() # None of the functions are completely nonlinear.
plt.figure(figsize=(20,12))
plt.title("Correlation Measurement")
sns.heatmap(data2[parameter_interest1].corr(),annot=True)
<AxesSubplot:title={'center':'Correlation Measurement'}>
plt.figure(figsize=(20,12))
plt.title("Correlation Measurement")
sns.heatmap(data2[parameter_interest1].cov(),annot=True)
<AxesSubplot:title={'center':'Correlation Measurement'}>
Thre is a only a very little difference between using Neural Network as a regressor and Neural network as a classifier. Only the output layer and the softmax function that changes the scenario. So,keeping that in the mind, we can first go for training it as a regressor, then we can make a small modification to our original data and make it as an classifier column and then go for the classifier Neural Network.
please note
Please note that the Object Oriented approach and the classification has been avoided completely and the process is simplified a lot. The approach is shifted to the part3 of the project where it becomes a necessity to go for class approach. The first two parts of the project that we are dealing with here are considered as a starting point, a stepping stone, for the part3 of the project. Hence, from the point of view of maximum flexibility and freedom from my perspective, it has been thought that these models will be envisaged to be used in that file as it is.
Approach Taken
Though there went a number of trials into developing these models, all of them are not noted or continued here. So, there are three trials with respect to both categorical and regressional approach to the data.
First Trial is without the validation set involved. Second trial is with the validation set invovled. Third trial is the performance metrics after performing some PCA because we saw some dimensions were pretty useless so, reduce the dimensions in the data and take thes same again. Fourth trial is nothing but one of the samples of the iterations that I took i.e. things like changing the number of layers, the batch size, the number of epochs etc.
from sklearn.model_selection import train_test_split
import tensorflow.keras as k
from tensorflow.keras.layers import Activation, Dense, Flatten
from tensorflow.keras import regularizers, optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Reshape, BatchNormalization, Dropout
X_train1, X_valid, y_train1, y_valid = train_test_split(data.drop("Signal_Strength",axis=1), data["Signal_Strength"], random_state=0)
######################################################
# Function Converting predicted probabilities into class labels.
######################################################
def convert_to_class_labels(y_predicted):
class_predicted=[]
y_pred=y_predicted
class_labels=y_pred.shape[1]
for each in y_pred :
array=each
for n in range(len(array)):
if array[n]==array.max():
class_predicted.append(n)
return class_predicted
########################################################
#
#######################################################
###################################################################
#Categorical Neural Network
###################################################################
model_cat_1=k.Sequential()
#model_cat_1.add(Flatten(input_shape=(X_train.shape[1],)))
#model_cat_1.add(Reshape((784,),input_shape=(X_train.shape[0],X_train.shape[1],)))
model_cat_1.add(BatchNormalization())
model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.2, input_shape=(60,)))
model_cat_1.add(Dense(30,activation="relu",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.2, input_shape=(30,)))
#model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.4, input_shape=(60,)))
#model_cat_1.add(Dense(60,activation="relu",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.2, input_shape=(60,)))
model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.4, input_shape=(60,)))
model_cat_1.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.2, input_shape=(30,)))
model_cat_1.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.2, input_shape=(30,)))
model_cat_1.add(Dense(15,activation="sigmoid",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dense(9,activation="softmax"))
sgd = optimizers.SGD(lr = 0.01,momentum=0.3)
model_cat_1.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.CategoricalAccuracy())
t=time.time()
###################################################################
#
###################################################################
history_cat_1=model_cat_1.fit(X_train1,k.utils.to_categorical(y_train1),batch_size=100, epochs = 500, verbose = 1)
print("Total Time Taken is : ",t-time.time())
Epoch 1/500
WARNING:tensorflow:Layer batch_normalization is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
12/12 [==============================] - 0s 1ms/step - loss: 0.2816 - categorical_accuracy: 0.1326
Epoch 2/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2802 - categorical_accuracy: 0.1326
Epoch 3/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2787 - categorical_accuracy: 0.1326
Epoch 4/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2773 - categorical_accuracy: 0.1326
Epoch 5/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2758 - categorical_accuracy: 0.1326
Epoch 6/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2744 - categorical_accuracy: 0.1326
Epoch 7/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2730 - categorical_accuracy: 0.1326
Epoch 8/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2716 - categorical_accuracy: 0.1326
Epoch 9/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2701 - categorical_accuracy: 0.1326
Epoch 10/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2688 - categorical_accuracy: 0.1326
Epoch 11/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2673 - categorical_accuracy: 0.1326
Epoch 12/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2660 - categorical_accuracy: 0.1326
Epoch 13/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2646 - categorical_accuracy: 0.1326
Epoch 14/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2633 - categorical_accuracy: 0.1326
Epoch 15/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2619 - categorical_accuracy: 0.1326
Epoch 16/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2606 - categorical_accuracy: 0.1326
Epoch 17/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2592 - categorical_accuracy: 0.1326
Epoch 18/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2579 - categorical_accuracy: 0.1326
Epoch 19/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2566 - categorical_accuracy: 0.1326
Epoch 20/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2553 - categorical_accuracy: 0.1326
Epoch 21/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2540 - categorical_accuracy: 0.1326
Epoch 22/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2527 - categorical_accuracy: 0.1326
Epoch 23/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2514 - categorical_accuracy: 0.1326
Epoch 24/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2502 - categorical_accuracy: 0.1326
Epoch 25/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2489 - categorical_accuracy: 0.1326
Epoch 26/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2477 - categorical_accuracy: 0.1393
Epoch 27/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2464 - categorical_accuracy: 0.1877
Epoch 28/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2452 - categorical_accuracy: 0.2711
Epoch 29/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2440 - categorical_accuracy: 0.3061
Epoch 30/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2427 - categorical_accuracy: 0.4053
Epoch 31/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2415 - categorical_accuracy: 0.4195
Epoch 32/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2403 - categorical_accuracy: 0.4279
Epoch 33/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2391 - categorical_accuracy: 0.4270
Epoch 34/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2380 - categorical_accuracy: 0.4270
Epoch 35/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2368 - categorical_accuracy: 0.4270
Epoch 36/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2357 - categorical_accuracy: 0.4270
Epoch 37/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2345 - categorical_accuracy: 0.4270
Epoch 38/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2333 - categorical_accuracy: 0.4270
Epoch 39/500
12/12 [==============================] - ETA: 0s - loss: 0.2312 - categorical_accuracy: 0.52 - 0s 1ms/step - loss: 0.2322 - categorical_accuracy: 0.4270
Epoch 40/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2310 - categorical_accuracy: 0.4270
Epoch 41/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2299 - categorical_accuracy: 0.4270
Epoch 42/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2288 - categorical_accuracy: 0.4270
Epoch 43/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2277 - categorical_accuracy: 0.4270
Epoch 44/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2266 - categorical_accuracy: 0.4270
Epoch 45/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2255 - categorical_accuracy: 0.4270
Epoch 46/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2244 - categorical_accuracy: 0.4270
Epoch 47/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2234 - categorical_accuracy: 0.4270
Epoch 48/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2223 - categorical_accuracy: 0.4270
Epoch 49/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2212 - categorical_accuracy: 0.4270
Epoch 50/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2202 - categorical_accuracy: 0.4270
Epoch 51/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2191 - categorical_accuracy: 0.4270
Epoch 52/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2181 - categorical_accuracy: 0.4270
Epoch 53/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2170 - categorical_accuracy: 0.4270
Epoch 54/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2160 - categorical_accuracy: 0.4270
Epoch 55/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2150 - categorical_accuracy: 0.4270
Epoch 56/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2140 - categorical_accuracy: 0.4270
Epoch 57/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2130 - categorical_accuracy: 0.4270
Epoch 58/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2120 - categorical_accuracy: 0.4270
Epoch 59/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2110 - categorical_accuracy: 0.4270
Epoch 60/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2100 - categorical_accuracy: 0.4270
Epoch 61/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2090 - categorical_accuracy: 0.4270
Epoch 62/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2081 - categorical_accuracy: 0.4270
Epoch 63/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2071 - categorical_accuracy: 0.4270
Epoch 64/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2062 - categorical_accuracy: 0.4270
Epoch 65/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2052 - categorical_accuracy: 0.4270
Epoch 66/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2043 - categorical_accuracy: 0.4270
Epoch 67/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2034 - categorical_accuracy: 0.4270
Epoch 68/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2024 - categorical_accuracy: 0.4270
Epoch 69/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2015 - categorical_accuracy: 0.4270
Epoch 70/500
12/12 [==============================] - 0s 1ms/step - loss: 0.2006 - categorical_accuracy: 0.4270
Epoch 71/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1997 - categorical_accuracy: 0.4270
Epoch 72/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1988 - categorical_accuracy: 0.4270
Epoch 73/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1979 - categorical_accuracy: 0.4270
Epoch 74/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1970 - categorical_accuracy: 0.4270
Epoch 75/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1961 - categorical_accuracy: 0.4270
Epoch 76/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1953 - categorical_accuracy: 0.4270
Epoch 77/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1944 - categorical_accuracy: 0.4270
Epoch 78/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1935 - categorical_accuracy: 0.4270
Epoch 79/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1927 - categorical_accuracy: 0.4270
Epoch 80/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1918 - categorical_accuracy: 0.4270
Epoch 81/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1910 - categorical_accuracy: 0.4270
Epoch 82/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1902 - categorical_accuracy: 0.4270
Epoch 83/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1894 - categorical_accuracy: 0.4270
Epoch 84/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1885 - categorical_accuracy: 0.4270
Epoch 85/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1877 - categorical_accuracy: 0.4270
Epoch 86/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1869 - categorical_accuracy: 0.4270
Epoch 87/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1861 - categorical_accuracy: 0.4270
Epoch 88/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1853 - categorical_accuracy: 0.4270
Epoch 89/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1845 - categorical_accuracy: 0.4270
Epoch 90/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1837 - categorical_accuracy: 0.4270
Epoch 91/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1829 - categorical_accuracy: 0.4270
Epoch 92/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1822 - categorical_accuracy: 0.4270
Epoch 93/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1814 - categorical_accuracy: 0.4270
Epoch 94/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1806 - categorical_accuracy: 0.4270
Epoch 95/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1799 - categorical_accuracy: 0.4270
Epoch 96/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1791 - categorical_accuracy: 0.4270
Epoch 97/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1783 - categorical_accuracy: 0.4270
Epoch 98/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1776 - categorical_accuracy: 0.4270
Epoch 99/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1769 - categorical_accuracy: 0.4270
Epoch 100/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1761 - categorical_accuracy: 0.4270
Epoch 101/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1754 - categorical_accuracy: 0.4270
Epoch 102/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1747 - categorical_accuracy: 0.4270
Epoch 103/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1740 - categorical_accuracy: 0.4270
Epoch 104/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1733 - categorical_accuracy: 0.4270
Epoch 105/500
12/12 [==============================] - ETA: 0s - loss: 0.1735 - categorical_accuracy: 0.39 - 0s 1ms/step - loss: 0.1726 - categorical_accuracy: 0.4270
Epoch 106/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1718 - categorical_accuracy: 0.4270
Epoch 107/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1712 - categorical_accuracy: 0.4270
Epoch 108/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1705 - categorical_accuracy: 0.4270
Epoch 109/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1698 - categorical_accuracy: 0.4270
Epoch 110/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1691 - categorical_accuracy: 0.4270
Epoch 111/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1684 - categorical_accuracy: 0.4270
Epoch 112/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1677 - categorical_accuracy: 0.4270
Epoch 113/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1671 - categorical_accuracy: 0.4270
Epoch 114/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1664 - categorical_accuracy: 0.4270
Epoch 115/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1657 - categorical_accuracy: 0.4270
Epoch 116/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1651 - categorical_accuracy: 0.4270
Epoch 117/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1644 - categorical_accuracy: 0.4270
Epoch 118/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1638 - categorical_accuracy: 0.4270
Epoch 119/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1632 - categorical_accuracy: 0.4270
Epoch 120/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1625 - categorical_accuracy: 0.4270
Epoch 121/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1619 - categorical_accuracy: 0.4270
Epoch 122/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1612 - categorical_accuracy: 0.4270
Epoch 123/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1606 - categorical_accuracy: 0.4270
Epoch 124/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1600 - categorical_accuracy: 0.4270
Epoch 125/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1594 - categorical_accuracy: 0.4270
Epoch 126/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1588 - categorical_accuracy: 0.4270
Epoch 127/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1582 - categorical_accuracy: 0.4270
Epoch 128/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1576 - categorical_accuracy: 0.4270
Epoch 129/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1570 - categorical_accuracy: 0.4270
Epoch 130/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1564 - categorical_accuracy: 0.4270
Epoch 131/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1558 - categorical_accuracy: 0.4270
Epoch 132/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1552 - categorical_accuracy: 0.4270
Epoch 133/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1547 - categorical_accuracy: 0.4270
Epoch 134/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1541 - categorical_accuracy: 0.4270
Epoch 135/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1535 - categorical_accuracy: 0.4270
Epoch 136/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1529 - categorical_accuracy: 0.4270
Epoch 137/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1524 - categorical_accuracy: 0.4270
Epoch 138/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1519 - categorical_accuracy: 0.4270
Epoch 139/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1513 - categorical_accuracy: 0.4270
Epoch 140/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1507 - categorical_accuracy: 0.4270
Epoch 141/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1502 - categorical_accuracy: 0.4270
Epoch 142/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1497 - categorical_accuracy: 0.4270
Epoch 143/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1491 - categorical_accuracy: 0.4270
Epoch 144/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1486 - categorical_accuracy: 0.4270
Epoch 145/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1480 - categorical_accuracy: 0.4270
Epoch 146/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1475 - categorical_accuracy: 0.4270
Epoch 147/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1470 - categorical_accuracy: 0.4270
Epoch 148/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1465 - categorical_accuracy: 0.4270
Epoch 149/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1460 - categorical_accuracy: 0.4270
Epoch 150/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1454 - categorical_accuracy: 0.4270
Epoch 151/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1449 - categorical_accuracy: 0.4270
Epoch 152/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1445 - categorical_accuracy: 0.4270
Epoch 153/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1439 - categorical_accuracy: 0.4270
Epoch 154/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1434 - categorical_accuracy: 0.4270
Epoch 155/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1429 - categorical_accuracy: 0.4270
Epoch 156/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1424 - categorical_accuracy: 0.4270
Epoch 157/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1419 - categorical_accuracy: 0.4270
Epoch 158/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1415 - categorical_accuracy: 0.4270
Epoch 159/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1410 - categorical_accuracy: 0.4270
Epoch 160/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1405 - categorical_accuracy: 0.4270
Epoch 161/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1400 - categorical_accuracy: 0.4270
Epoch 162/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1396 - categorical_accuracy: 0.4270
Epoch 163/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1391 - categorical_accuracy: 0.4270
Epoch 164/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1386 - categorical_accuracy: 0.4270
Epoch 165/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1382 - categorical_accuracy: 0.4270
Epoch 166/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1377 - categorical_accuracy: 0.4270
Epoch 167/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1373 - categorical_accuracy: 0.4270
Epoch 168/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1368 - categorical_accuracy: 0.4270
Epoch 169/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1364 - categorical_accuracy: 0.4270
Epoch 170/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1359 - categorical_accuracy: 0.4270
Epoch 171/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1355 - categorical_accuracy: 0.4270
Epoch 172/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1351 - categorical_accuracy: 0.4270
Epoch 173/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1346 - categorical_accuracy: 0.4270
Epoch 174/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1342 - categorical_accuracy: 0.4270
Epoch 175/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1338 - categorical_accuracy: 0.4270
Epoch 176/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1333 - categorical_accuracy: 0.4270
Epoch 177/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1329 - categorical_accuracy: 0.4270
Epoch 178/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1325 - categorical_accuracy: 0.4270
Epoch 179/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1321 - categorical_accuracy: 0.4270
Epoch 180/500
12/12 [==============================] - 0s 997us/step - loss: 0.1316 - categorical_accuracy: 0.4270
Epoch 181/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1313 - categorical_accuracy: 0.4270
Epoch 182/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1308 - categorical_accuracy: 0.4270
Epoch 183/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1304 - categorical_accuracy: 0.4270
Epoch 184/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1300 - categorical_accuracy: 0.4270
Epoch 185/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1296 - categorical_accuracy: 0.4270
Epoch 186/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1292 - categorical_accuracy: 0.4270
Epoch 187/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1288 - categorical_accuracy: 0.4270
Epoch 188/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1284 - categorical_accuracy: 0.4270
Epoch 189/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1281 - categorical_accuracy: 0.4270
Epoch 190/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1277 - categorical_accuracy: 0.4270
Epoch 191/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1273 - categorical_accuracy: 0.4270
Epoch 192/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1269 - categorical_accuracy: 0.4270
Epoch 193/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1266 - categorical_accuracy: 0.4270
Epoch 194/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1262 - categorical_accuracy: 0.4270
Epoch 195/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1258 - categorical_accuracy: 0.4270
Epoch 196/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1254 - categorical_accuracy: 0.4270
Epoch 197/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1251 - categorical_accuracy: 0.4270
Epoch 198/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1247 - categorical_accuracy: 0.4270
Epoch 199/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1243 - categorical_accuracy: 0.4270
Epoch 200/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1240 - categorical_accuracy: 0.4270
Epoch 201/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1236 - categorical_accuracy: 0.4270
Epoch 202/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1233 - categorical_accuracy: 0.4270
Epoch 203/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1229 - categorical_accuracy: 0.4270
Epoch 204/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1226 - categorical_accuracy: 0.4270
Epoch 205/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1222 - categorical_accuracy: 0.4270
Epoch 206/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1219 - categorical_accuracy: 0.4270
Epoch 207/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1216 - categorical_accuracy: 0.4270
Epoch 208/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1212 - categorical_accuracy: 0.4270
Epoch 209/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1209 - categorical_accuracy: 0.4270
Epoch 210/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1205 - categorical_accuracy: 0.4270
Epoch 211/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1202 - categorical_accuracy: 0.4270
Epoch 212/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1199 - categorical_accuracy: 0.4270
Epoch 213/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1196 - categorical_accuracy: 0.4270
Epoch 214/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1192 - categorical_accuracy: 0.4270
Epoch 215/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1189 - categorical_accuracy: 0.4270
Epoch 216/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1186 - categorical_accuracy: 0.4270
Epoch 217/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1183 - categorical_accuracy: 0.4270
Epoch 218/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1180 - categorical_accuracy: 0.4270
Epoch 219/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1176 - categorical_accuracy: 0.4270
Epoch 220/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1173 - categorical_accuracy: 0.4270
Epoch 221/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1170 - categorical_accuracy: 0.4270
Epoch 222/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1167 - categorical_accuracy: 0.4270
Epoch 223/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1164 - categorical_accuracy: 0.4270
Epoch 224/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1161 - categorical_accuracy: 0.4270
Epoch 225/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1158 - categorical_accuracy: 0.4270
Epoch 226/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1155 - categorical_accuracy: 0.4270
Epoch 227/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1153 - categorical_accuracy: 0.4270
Epoch 228/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1150 - categorical_accuracy: 0.4270
Epoch 229/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1146 - categorical_accuracy: 0.4270
Epoch 230/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1144 - categorical_accuracy: 0.4270
Epoch 231/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1141 - categorical_accuracy: 0.4270
Epoch 232/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1138 - categorical_accuracy: 0.4270
Epoch 233/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1135 - categorical_accuracy: 0.4270
Epoch 234/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1132 - categorical_accuracy: 0.4270
Epoch 235/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1130 - categorical_accuracy: 0.4270
Epoch 236/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1127 - categorical_accuracy: 0.4270
Epoch 237/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1124 - categorical_accuracy: 0.4270
Epoch 238/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1121 - categorical_accuracy: 0.4270
Epoch 239/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1118 - categorical_accuracy: 0.4270
Epoch 240/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1116 - categorical_accuracy: 0.4270
Epoch 241/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1113 - categorical_accuracy: 0.4270
Epoch 242/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1110 - categorical_accuracy: 0.4270
Epoch 243/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1108 - categorical_accuracy: 0.4270
Epoch 244/500
12/12 [==============================] - 0s 997us/step - loss: 0.1105 - categorical_accuracy: 0.4270
Epoch 245/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1103 - categorical_accuracy: 0.4270
Epoch 246/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1100 - categorical_accuracy: 0.4270
Epoch 247/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1097 - categorical_accuracy: 0.4270
Epoch 248/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1095 - categorical_accuracy: 0.4270
Epoch 249/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1093 - categorical_accuracy: 0.4270
Epoch 250/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1090 - categorical_accuracy: 0.4270
Epoch 251/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1088 - categorical_accuracy: 0.4270
Epoch 252/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1085 - categorical_accuracy: 0.4270
Epoch 253/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1083 - categorical_accuracy: 0.4270
Epoch 254/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1080 - categorical_accuracy: 0.4270
Epoch 255/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1078 - categorical_accuracy: 0.4270
Epoch 256/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1075 - categorical_accuracy: 0.4270
Epoch 257/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1073 - categorical_accuracy: 0.4270
Epoch 258/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1071 - categorical_accuracy: 0.4270
Epoch 259/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1068 - categorical_accuracy: 0.4270
Epoch 260/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1066 - categorical_accuracy: 0.4270
Epoch 261/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1064 - categorical_accuracy: 0.4270
Epoch 262/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1061 - categorical_accuracy: 0.4270
Epoch 263/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1059 - categorical_accuracy: 0.4270
Epoch 264/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1057 - categorical_accuracy: 0.4270
Epoch 265/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1054 - categorical_accuracy: 0.4270
Epoch 266/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1052 - categorical_accuracy: 0.4270
Epoch 267/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1050 - categorical_accuracy: 0.4270
Epoch 268/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1048 - categorical_accuracy: 0.4270
Epoch 269/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1046 - categorical_accuracy: 0.4270
Epoch 270/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1043 - categorical_accuracy: 0.4270
Epoch 271/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1041 - categorical_accuracy: 0.4270
Epoch 272/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1039 - categorical_accuracy: 0.4270
Epoch 273/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1037 - categorical_accuracy: 0.4270
Epoch 274/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1035 - categorical_accuracy: 0.4270
Epoch 275/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1033 - categorical_accuracy: 0.4270
Epoch 276/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1031 - categorical_accuracy: 0.4270
Epoch 277/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1029 - categorical_accuracy: 0.4270
Epoch 278/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1027 - categorical_accuracy: 0.4270
Epoch 279/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1025 - categorical_accuracy: 0.4270
Epoch 280/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1023 - categorical_accuracy: 0.4270
Epoch 281/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1020 - categorical_accuracy: 0.4270
Epoch 282/500
12/12 [==============================] - 0s 997us/step - loss: 0.1018 - categorical_accuracy: 0.4270
Epoch 283/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1016 - categorical_accuracy: 0.4270
Epoch 284/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1015 - categorical_accuracy: 0.4270
Epoch 285/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1013 - categorical_accuracy: 0.4270
Epoch 286/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1011 - categorical_accuracy: 0.4270
Epoch 287/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1009 - categorical_accuracy: 0.4270
Epoch 288/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1007 - categorical_accuracy: 0.4270
Epoch 289/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1005 - categorical_accuracy: 0.4270
Epoch 290/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1003 - categorical_accuracy: 0.4270
Epoch 291/500
12/12 [==============================] - 0s 1ms/step - loss: 0.1001 - categorical_accuracy: 0.4270
Epoch 292/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0999 - categorical_accuracy: 0.4270
Epoch 293/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0998 - categorical_accuracy: 0.4270
Epoch 294/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0995 - categorical_accuracy: 0.4270
Epoch 295/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0994 - categorical_accuracy: 0.4270
Epoch 296/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0992 - categorical_accuracy: 0.4270
Epoch 297/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0990 - categorical_accuracy: 0.4270
Epoch 298/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0988 - categorical_accuracy: 0.4270
Epoch 299/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0987 - categorical_accuracy: 0.4270
Epoch 300/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0985 - categorical_accuracy: 0.4270
Epoch 301/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0983 - categorical_accuracy: 0.4270
Epoch 302/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0981 - categorical_accuracy: 0.4270
Epoch 303/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0980 - categorical_accuracy: 0.4270
Epoch 304/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0978 - categorical_accuracy: 0.4270
Epoch 305/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0976 - categorical_accuracy: 0.4270
Epoch 306/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0975 - categorical_accuracy: 0.4270
Epoch 307/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0973 - categorical_accuracy: 0.4270
Epoch 308/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0971 - categorical_accuracy: 0.4270
Epoch 309/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0969 - categorical_accuracy: 0.4270
Epoch 310/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0968 - categorical_accuracy: 0.4270
Epoch 311/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0966 - categorical_accuracy: 0.4270
Epoch 312/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0965 - categorical_accuracy: 0.4270
Epoch 313/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0963 - categorical_accuracy: 0.4270
Epoch 314/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0961 - categorical_accuracy: 0.4270
Epoch 315/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0960 - categorical_accuracy: 0.4270
Epoch 316/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0958 - categorical_accuracy: 0.4270
Epoch 317/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0957 - categorical_accuracy: 0.4270
Epoch 318/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0955 - categorical_accuracy: 0.4270
Epoch 319/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0953 - categorical_accuracy: 0.4270
Epoch 320/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0952 - categorical_accuracy: 0.4270
Epoch 321/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0951 - categorical_accuracy: 0.4270
Epoch 322/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0949 - categorical_accuracy: 0.4270
Epoch 323/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0947 - categorical_accuracy: 0.4270
Epoch 324/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0946 - categorical_accuracy: 0.4270
Epoch 325/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0944 - categorical_accuracy: 0.4270
Epoch 326/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0943 - categorical_accuracy: 0.4270
Epoch 327/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0942 - categorical_accuracy: 0.4270
Epoch 328/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0940 - categorical_accuracy: 0.4270
Epoch 329/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0939 - categorical_accuracy: 0.4270
Epoch 330/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0937 - categorical_accuracy: 0.4270
Epoch 331/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0936 - categorical_accuracy: 0.4270
Epoch 332/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0934 - categorical_accuracy: 0.4270
Epoch 333/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0933 - categorical_accuracy: 0.4270
Epoch 334/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0932 - categorical_accuracy: 0.4270
Epoch 335/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0930 - categorical_accuracy: 0.4270
Epoch 336/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0929 - categorical_accuracy: 0.4270
Epoch 337/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0928 - categorical_accuracy: 0.4270
Epoch 338/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0926 - categorical_accuracy: 0.4270
Epoch 339/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0925 - categorical_accuracy: 0.4270
Epoch 340/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0923 - categorical_accuracy: 0.4270
Epoch 341/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0922 - categorical_accuracy: 0.4270
Epoch 342/500
12/12 [==============================] - 0s 997us/step - loss: 0.0921 - categorical_accuracy: 0.4270
Epoch 343/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0919 - categorical_accuracy: 0.4270
Epoch 344/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0918 - categorical_accuracy: 0.4270
Epoch 345/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0917 - categorical_accuracy: 0.4270
Epoch 346/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0916 - categorical_accuracy: 0.4270
Epoch 347/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0914 - categorical_accuracy: 0.4270
Epoch 348/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0913 - categorical_accuracy: 0.4270
Epoch 349/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0912 - categorical_accuracy: 0.4270
Epoch 350/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0911 - categorical_accuracy: 0.4270
Epoch 351/500
12/12 [==============================] - 0s 997us/step - loss: 0.0910 - categorical_accuracy: 0.4270
Epoch 352/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0908 - categorical_accuracy: 0.4270
Epoch 353/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0907 - categorical_accuracy: 0.4270
Epoch 354/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0906 - categorical_accuracy: 0.4270
Epoch 355/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0905 - categorical_accuracy: 0.4270
Epoch 356/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0903 - categorical_accuracy: 0.4270
Epoch 357/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0902 - categorical_accuracy: 0.4270
Epoch 358/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0901 - categorical_accuracy: 0.4270
Epoch 359/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0900 - categorical_accuracy: 0.4270
Epoch 360/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0898 - categorical_accuracy: 0.4270
Epoch 361/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0897 - categorical_accuracy: 0.4270
Epoch 362/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0896 - categorical_accuracy: 0.4270
Epoch 363/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0895 - categorical_accuracy: 0.4270
Epoch 364/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0894 - categorical_accuracy: 0.4270
Epoch 365/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0893 - categorical_accuracy: 0.4270
Epoch 366/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0892 - categorical_accuracy: 0.4270
Epoch 367/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0891 - categorical_accuracy: 0.4270
Epoch 368/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0889 - categorical_accuracy: 0.4270
Epoch 369/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0888 - categorical_accuracy: 0.4270
Epoch 370/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0887 - categorical_accuracy: 0.4270
Epoch 371/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0886 - categorical_accuracy: 0.4270
Epoch 372/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0885 - categorical_accuracy: 0.4270
Epoch 373/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0884 - categorical_accuracy: 0.4270
Epoch 374/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0883 - categorical_accuracy: 0.4270
Epoch 375/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0882 - categorical_accuracy: 0.4270
Epoch 376/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0881 - categorical_accuracy: 0.4270
Epoch 377/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0880 - categorical_accuracy: 0.4270
Epoch 378/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0879 - categorical_accuracy: 0.4270
Epoch 379/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0878 - categorical_accuracy: 0.4270
Epoch 380/500
12/12 [==============================] - ETA: 0s - loss: 0.0868 - categorical_accuracy: 0.44 - 0s 1ms/step - loss: 0.0877 - categorical_accuracy: 0.4270
Epoch 381/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0876 - categorical_accuracy: 0.4270
Epoch 382/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0875 - categorical_accuracy: 0.4270
Epoch 383/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0874 - categorical_accuracy: 0.4270
Epoch 384/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0873 - categorical_accuracy: 0.4270
Epoch 385/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0872 - categorical_accuracy: 0.4270
Epoch 386/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0871 - categorical_accuracy: 0.4270
Epoch 387/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0870 - categorical_accuracy: 0.4270
Epoch 388/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0869 - categorical_accuracy: 0.4270
Epoch 389/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0868 - categorical_accuracy: 0.4270
Epoch 390/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0867 - categorical_accuracy: 0.4270
Epoch 391/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0866 - categorical_accuracy: 0.4270
Epoch 392/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0865 - categorical_accuracy: 0.4270
Epoch 393/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0864 - categorical_accuracy: 0.4270
Epoch 394/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0863 - categorical_accuracy: 0.4270
Epoch 395/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0863 - categorical_accuracy: 0.4270
Epoch 396/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0861 - categorical_accuracy: 0.4270
Epoch 397/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0861 - categorical_accuracy: 0.4270
Epoch 398/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0860 - categorical_accuracy: 0.4270
Epoch 399/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0859 - categorical_accuracy: 0.4270
Epoch 400/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0858 - categorical_accuracy: 0.4270
Epoch 401/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0857 - categorical_accuracy: 0.4270
Epoch 402/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0856 - categorical_accuracy: 0.4270
Epoch 403/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0855 - categorical_accuracy: 0.4270
Epoch 404/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0855 - categorical_accuracy: 0.4270
Epoch 405/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0854 - categorical_accuracy: 0.4270
Epoch 406/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0853 - categorical_accuracy: 0.4270
Epoch 407/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0852 - categorical_accuracy: 0.4270
Epoch 408/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0851 - categorical_accuracy: 0.4270
Epoch 409/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0850 - categorical_accuracy: 0.4270
Epoch 410/500
12/12 [==============================] - 0s 997us/step - loss: 0.0849 - categorical_accuracy: 0.4270
Epoch 411/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0849 - categorical_accuracy: 0.4270
Epoch 412/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0848 - categorical_accuracy: 0.4270
Epoch 413/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0847 - categorical_accuracy: 0.4270
Epoch 414/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0846 - categorical_accuracy: 0.4270
Epoch 415/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0845 - categorical_accuracy: 0.4270
Epoch 416/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0845 - categorical_accuracy: 0.4270
Epoch 417/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0844 - categorical_accuracy: 0.4270
Epoch 418/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0843 - categorical_accuracy: 0.4270
Epoch 419/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0842 - categorical_accuracy: 0.4270
Epoch 420/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0841 - categorical_accuracy: 0.4270
Epoch 421/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0841 - categorical_accuracy: 0.4270
Epoch 422/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0840 - categorical_accuracy: 0.4270
Epoch 423/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0839 - categorical_accuracy: 0.4270
Epoch 424/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0839 - categorical_accuracy: 0.4270
Epoch 425/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0838 - categorical_accuracy: 0.4270
Epoch 426/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0837 - categorical_accuracy: 0.4270
Epoch 427/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0836 - categorical_accuracy: 0.4270
Epoch 428/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0835 - categorical_accuracy: 0.4270
Epoch 429/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0834 - categorical_accuracy: 0.4270
Epoch 430/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0834 - categorical_accuracy: 0.4270
Epoch 431/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0833 - categorical_accuracy: 0.4270
Epoch 432/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0833 - categorical_accuracy: 0.4270
Epoch 433/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0832 - categorical_accuracy: 0.4270
Epoch 434/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0831 - categorical_accuracy: 0.4270
Epoch 435/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0830 - categorical_accuracy: 0.4270
Epoch 436/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0830 - categorical_accuracy: 0.4270
Epoch 437/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0829 - categorical_accuracy: 0.4270
Epoch 438/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0828 - categorical_accuracy: 0.4270
Epoch 439/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0828 - categorical_accuracy: 0.4270
Epoch 440/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0827 - categorical_accuracy: 0.4270
Epoch 441/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0827 - categorical_accuracy: 0.4270
Epoch 442/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0826 - categorical_accuracy: 0.4270
Epoch 443/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0825 - categorical_accuracy: 0.4270
Epoch 444/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0824 - categorical_accuracy: 0.4270
Epoch 445/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0824 - categorical_accuracy: 0.4270
Epoch 446/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0823 - categorical_accuracy: 0.4270
Epoch 447/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0822 - categorical_accuracy: 0.4270
Epoch 448/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0822 - categorical_accuracy: 0.4270
Epoch 449/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0821 - categorical_accuracy: 0.4270
Epoch 450/500
12/12 [==============================] - ETA: 0s - loss: 0.0805 - categorical_accuracy: 0.45 - 0s 1ms/step - loss: 0.0820 - categorical_accuracy: 0.4270
Epoch 451/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0820 - categorical_accuracy: 0.4270
Epoch 452/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0819 - categorical_accuracy: 0.4270
Epoch 453/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0819 - categorical_accuracy: 0.4270
Epoch 454/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0818 - categorical_accuracy: 0.4270
Epoch 455/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0817 - categorical_accuracy: 0.4270
Epoch 456/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0817 - categorical_accuracy: 0.4270
Epoch 457/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0816 - categorical_accuracy: 0.4270
Epoch 458/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0815 - categorical_accuracy: 0.4270
Epoch 459/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0815 - categorical_accuracy: 0.4270
Epoch 460/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0814 - categorical_accuracy: 0.4270
Epoch 461/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0814 - categorical_accuracy: 0.4270
Epoch 462/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0813 - categorical_accuracy: 0.4270
Epoch 463/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0813 - categorical_accuracy: 0.4270
Epoch 464/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0812 - categorical_accuracy: 0.4270
Epoch 465/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0811 - categorical_accuracy: 0.4270
Epoch 466/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0811 - categorical_accuracy: 0.4270
Epoch 467/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0810 - categorical_accuracy: 0.4270
Epoch 468/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0810 - categorical_accuracy: 0.4270
Epoch 469/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0809 - categorical_accuracy: 0.4270
Epoch 470/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0809 - categorical_accuracy: 0.4270
Epoch 471/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0808 - categorical_accuracy: 0.4270
Epoch 472/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0808 - categorical_accuracy: 0.4270
Epoch 473/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0807 - categorical_accuracy: 0.4270
Epoch 474/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0807 - categorical_accuracy: 0.4270
Epoch 475/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0806 - categorical_accuracy: 0.4270
Epoch 476/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0805 - categorical_accuracy: 0.4270
Epoch 477/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0805 - categorical_accuracy: 0.4270
Epoch 478/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0804 - categorical_accuracy: 0.4270
Epoch 479/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0804 - categorical_accuracy: 0.4270
Epoch 480/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0803 - categorical_accuracy: 0.4270
Epoch 481/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0803 - categorical_accuracy: 0.4270
Epoch 482/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0802 - categorical_accuracy: 0.4270
Epoch 483/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0802 - categorical_accuracy: 0.4270
Epoch 484/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0801 - categorical_accuracy: 0.4270
Epoch 485/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0801 - categorical_accuracy: 0.4270
Epoch 486/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0800 - categorical_accuracy: 0.4270
Epoch 487/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0800 - categorical_accuracy: 0.4270
Epoch 488/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0799 - categorical_accuracy: 0.4270
Epoch 489/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0799 - categorical_accuracy: 0.4270
Epoch 490/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0798 - categorical_accuracy: 0.4270
Epoch 491/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0798 - categorical_accuracy: 0.4270
Epoch 492/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 493/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 494/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 495/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0796 - categorical_accuracy: 0.4270
Epoch 496/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0796 - categorical_accuracy: 0.4270
Epoch 497/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 498/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 499/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0794 - categorical_accuracy: 0.4270
Epoch 500/500
12/12 [==============================] - 0s 1ms/step - loss: 0.0794 - categorical_accuracy: 0.4270
Total Time Taken is : -9.994266033172607
labels=y_train1.astype("category").dtype.categories
from sklearn.preprocessing import OneHotEncoder as encode
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
####################################################
y_pred_cat_1=model_cat_1.predict(X_valid)
y_pred_cat_1
array([[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396],
[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396],
[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396],
...,
[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396],
[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396],
[0.02683579, 0.03718928, 0.02917883, ..., 0.34378353, 0.0881912 ,
0.02797396]], dtype=float32)
print("The Accuracy of the model is : ",accuracy_score(y_valid,convert_to_class_labels(y_pred_cat_1)))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_valid,convert_to_class_labels(y_pred_cat_1)),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.4225
history=history_cat_1.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["categorical_accuracy"])
ax.set_title("Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'categorical_accuracy'])
###################################################################
#Regressional Neural Network
###################################################################
model_reg_1=k.Sequential()
model_reg_1.add(BatchNormalization(input_shape=(X_train1.shape[1],)))
model_reg_1.add(Flatten())
model_reg_1.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1.add(Dropout(0.2, input_shape=(50,)))
model_reg_1.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1.add(Dropout(0.2, input_shape=(50,)))
model_reg_1.add(Dense(30,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1.add(Dropout(0.5, input_shape=(50,)))
#model_reg_1.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1.add(Dropout(0.2, input_shape=(50,)))
#model_reg_1.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1.add(Dropout(0.5, input_shape=(30,)))
model_reg_1.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1.add(Dense(1))
sgd = optimizers.SGD(lr = 0.01,momentum=0.6)
model_reg_1.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.MeanSquaredError())
###################################################################
#
###################################################################
t=time.time()
history_reg_1=model_reg_1.fit(X_train1,y_train1,batch_size=100, epochs = 500) #add verbose later
print("Total Time Taken is : ",t-time.time())
Epoch 1/500 12/12 [==============================] - 0s 914us/step - loss: 15.0834 - mean_squared_error: 15.0213 Epoch 2/500 12/12 [==============================] - 0s 831us/step - loss: 0.8006 - mean_squared_error: 0.7129 Epoch 3/500 12/12 [==============================] - 0s 997us/step - loss: 0.7565 - mean_squared_error: 0.6710 Epoch 4/500 12/12 [==============================] - 0s 997us/step - loss: 0.7564 - mean_squared_error: 0.6714 Epoch 5/500 12/12 [==============================] - 0s 914us/step - loss: 0.7553 - mean_squared_error: 0.6711 Epoch 6/500 12/12 [==============================] - 0s 997us/step - loss: 0.7522 - mean_squared_error: 0.6690 Epoch 7/500 12/12 [==============================] - 0s 914us/step - loss: 0.7536 - mean_squared_error: 0.6712 Epoch 8/500 12/12 [==============================] - 0s 914us/step - loss: 0.7467 - mean_squared_error: 0.6649 Epoch 9/500 12/12 [==============================] - 0s 914us/step - loss: 0.7477 - mean_squared_error: 0.6670 Epoch 10/500 12/12 [==============================] - 0s 914us/step - loss: 0.7454 - mean_squared_error: 0.6652 Epoch 11/500 12/12 [==============================] - 0s 997us/step - loss: 0.7443 - mean_squared_error: 0.6650 Epoch 12/500 12/12 [==============================] - 0s 914us/step - loss: 0.7456 - mean_squared_error: 0.6668 Epoch 13/500 12/12 [==============================] - 0s 914us/step - loss: 0.7479 - mean_squared_error: 0.6703 Epoch 14/500 12/12 [==============================] - 0s 914us/step - loss: 0.7437 - mean_squared_error: 0.6663 Epoch 15/500 12/12 [==============================] - 0s 914us/step - loss: 0.7445 - mean_squared_error: 0.6683 Epoch 16/500 12/12 [==============================] - 0s 914us/step - loss: 0.7424 - mean_squared_error: 0.6666 Epoch 17/500 12/12 [==============================] - 0s 997us/step - loss: 0.7447 - mean_squared_error: 0.6697 Epoch 18/500 12/12 [==============================] - 0s 997us/step - loss: 0.7402 - mean_squared_error: 0.6660 Epoch 19/500 12/12 [==============================] - 0s 914us/step - loss: 0.7407 - mean_squared_error: 0.6671 Epoch 20/500 12/12 [==============================] - 0s 997us/step - loss: 0.7401 - mean_squared_error: 0.6672 Epoch 21/500 12/12 [==============================] - 0s 997us/step - loss: 0.7377 - mean_squared_error: 0.6654 Epoch 22/500 12/12 [==============================] - 0s 997us/step - loss: 0.7361 - mean_squared_error: 0.6644 Epoch 23/500 12/12 [==============================] - 0s 914us/step - loss: 0.7391 - mean_squared_error: 0.6682 Epoch 24/500 12/12 [==============================] - 0s 914us/step - loss: 0.7347 - mean_squared_error: 0.6643 Epoch 25/500 12/12 [==============================] - 0s 997us/step - loss: 0.7358 - mean_squared_error: 0.6663 Epoch 26/500 12/12 [==============================] - 0s 914us/step - loss: 0.7334 - mean_squared_error: 0.6644 Epoch 27/500 12/12 [==============================] - 0s 997us/step - loss: 0.7351 - mean_squared_error: 0.6666 Epoch 28/500 12/12 [==============================] - 0s 997us/step - loss: 0.7349 - mean_squared_error: 0.6672 Epoch 29/500 12/12 [==============================] - 0s 997us/step - loss: 0.7371 - mean_squared_error: 0.6699 Epoch 30/500 12/12 [==============================] - 0s 997us/step - loss: 0.7336 - mean_squared_error: 0.6671 Epoch 31/500 12/12 [==============================] - 0s 997us/step - loss: 0.7314 - mean_squared_error: 0.6653 Epoch 32/500 12/12 [==============================] - 0s 997us/step - loss: 0.7323 - mean_squared_error: 0.6669 Epoch 33/500 12/12 [==============================] - 0s 997us/step - loss: 0.7297 - mean_squared_error: 0.6648 Epoch 34/500 12/12 [==============================] - 0s 997us/step - loss: 0.7319 - mean_squared_error: 0.6677 Epoch 35/500 12/12 [==============================] - 0s 1ms/step - loss: 0.7328 - mean_squared_error: 0.6692 Epoch 36/500 12/12 [==============================] - 0s 1ms/step - loss: 0.7305 - mean_squared_error: 0.6673 Epoch 37/500 12/12 [==============================] - 0s 997us/step - loss: 0.7293 - mean_squared_error: 0.6668 Epoch 38/500 12/12 [==============================] - 0s 997us/step - loss: 0.7270 - mean_squared_error: 0.6649 Epoch 39/500 12/12 [==============================] - 0s 997us/step - loss: 0.7272 - mean_squared_error: 0.6657 Epoch 40/500 12/12 [==============================] - 0s 914us/step - loss: 0.7261 - mean_squared_error: 0.6654 Epoch 41/500 12/12 [==============================] - 0s 997us/step - loss: 0.7292 - mean_squared_error: 0.6687 Epoch 42/500 12/12 [==============================] - 0s 997us/step - loss: 0.7256 - mean_squared_error: 0.6655 Epoch 43/500 12/12 [==============================] - 0s 914us/step - loss: 0.7261 - mean_squared_error: 0.6667 Epoch 44/500 12/12 [==============================] - 0s 914us/step - loss: 0.7262 - mean_squared_error: 0.6673 Epoch 45/500 12/12 [==============================] - 0s 914us/step - loss: 0.7245 - mean_squared_error: 0.6660 Epoch 46/500 12/12 [==============================] - 0s 914us/step - loss: 0.7216 - mean_squared_error: 0.6637 Epoch 47/500 12/12 [==============================] - 0s 914us/step - loss: 0.7247 - mean_squared_error: 0.6672 Epoch 48/500 12/12 [==============================] - 0s 997us/step - loss: 0.7239 - mean_squared_error: 0.6669 Epoch 49/500 12/12 [==============================] - 0s 914us/step - loss: 0.7238 - mean_squared_error: 0.6674 Epoch 50/500 12/12 [==============================] - 0s 997us/step - loss: 0.7225 - mean_squared_error: 0.6664 Epoch 51/500 12/12 [==============================] - 0s 997us/step - loss: 0.7205 - mean_squared_error: 0.6649 Epoch 52/500 12/12 [==============================] - 0s 914us/step - loss: 0.7211 - mean_squared_error: 0.6661 Epoch 53/500 12/12 [==============================] - 0s 914us/step - loss: 0.7200 - mean_squared_error: 0.6652 Epoch 54/500 12/12 [==============================] - 0s 997us/step - loss: 0.7197 - mean_squared_error: 0.6655 Epoch 55/500 12/12 [==============================] - 0s 997us/step - loss: 0.7202 - mean_squared_error: 0.6663 Epoch 56/500 12/12 [==============================] - 0s 914us/step - loss: 0.7182 - mean_squared_error: 0.6648 Epoch 57/500 12/12 [==============================] - 0s 914us/step - loss: 0.7171 - mean_squared_error: 0.6641 Epoch 58/500 12/12 [==============================] - 0s 914us/step - loss: 0.7171 - mean_squared_error: 0.6645 Epoch 59/500 12/12 [==============================] - 0s 831us/step - loss: 0.7169 - mean_squared_error: 0.6647 Epoch 60/500 12/12 [==============================] - 0s 1ms/step - loss: 0.7147 - mean_squared_error: 0.6629 Epoch 61/500 12/12 [==============================] - 0s 1ms/step - loss: 0.7161 - mean_squared_error: 0.6648 Epoch 62/500 12/12 [==============================] - 0s 997us/step - loss: 0.7158 - mean_squared_error: 0.6648 Epoch 63/500 12/12 [==============================] - 0s 997us/step - loss: 0.7146 - mean_squared_error: 0.6638 Epoch 64/500 12/12 [==============================] - 0s 1ms/step - loss: 0.7160 - mean_squared_error: 0.6659 Epoch 65/500 12/12 [==============================] - 0s 997us/step - loss: 0.7143 - mean_squared_error: 0.6643 Epoch 66/500 12/12 [==============================] - ETA: 0s - loss: 0.9406 - mean_squared_error: 0.89 - 0s 997us/step - loss: 0.7121 - mean_squared_error: 0.6626 Epoch 67/500 12/12 [==============================] - 0s 997us/step - loss: 0.7115 - mean_squared_error: 0.6623 Epoch 68/500 12/12 [==============================] - 0s 997us/step - loss: 0.7110 - mean_squared_error: 0.6620 Epoch 69/500 12/12 [==============================] - 0s 997us/step - loss: 0.7099 - mean_squared_error: 0.6615 Epoch 70/500 12/12 [==============================] - 0s 997us/step - loss: 0.7088 - mean_squared_error: 0.6604 Epoch 71/500 12/12 [==============================] - 0s 997us/step - loss: 0.7096 - mean_squared_error: 0.6616 Epoch 72/500 12/12 [==============================] - 0s 997us/step - loss: 0.7107 - mean_squared_error: 0.6631 Epoch 73/500 12/12 [==============================] - 0s 914us/step - loss: 0.7069 - mean_squared_error: 0.6594 Epoch 74/500 12/12 [==============================] - 0s 914us/step - loss: 0.7079 - mean_squared_error: 0.6607 Epoch 75/500 12/12 [==============================] - 0s 997us/step - loss: 0.7052 - mean_squared_error: 0.6583 Epoch 76/500 12/12 [==============================] - 0s 997us/step - loss: 0.7050 - mean_squared_error: 0.6583 Epoch 77/500 12/12 [==============================] - 0s 997us/step - loss: 0.7036 - mean_squared_error: 0.6571 Epoch 78/500 12/12 [==============================] - 0s 997us/step - loss: 0.6978 - mean_squared_error: 0.6518 Epoch 79/500 12/12 [==============================] - 0s 914us/step - loss: 0.6988 - mean_squared_error: 0.6522 Epoch 80/500 12/12 [==============================] - 0s 1ms/step - loss: 0.6942 - mean_squared_error: 0.6481 Epoch 81/500 12/12 [==============================] - 0s 997us/step - loss: 0.6944 - mean_squared_error: 0.6483 Epoch 82/500 12/12 [==============================] - 0s 997us/step - loss: 0.6907 - mean_squared_error: 0.6444 Epoch 83/500 12/12 [==============================] - 0s 997us/step - loss: 0.6825 - mean_squared_error: 0.6363 Epoch 84/500 12/12 [==============================] - 0s 914us/step - loss: 0.6836 - mean_squared_error: 0.6370 Epoch 85/500 12/12 [==============================] - 0s 914us/step - loss: 0.6703 - mean_squared_error: 0.6233 Epoch 86/500 12/12 [==============================] - 0s 914us/step - loss: 0.6568 - mean_squared_error: 0.6094 Epoch 87/500 12/12 [==============================] - 0s 914us/step - loss: 0.6443 - mean_squared_error: 0.5962 Epoch 88/500 12/12 [==============================] - 0s 831us/step - loss: 0.6313 - mean_squared_error: 0.5824 Epoch 89/500 12/12 [==============================] - 0s 831us/step - loss: 0.6028 - mean_squared_error: 0.5532 Epoch 90/500 12/12 [==============================] - 0s 831us/step - loss: 0.5995 - mean_squared_error: 0.5487 Epoch 91/500 12/12 [==============================] - 0s 827us/step - loss: 0.5707 - mean_squared_error: 0.5192 Epoch 92/500 12/12 [==============================] - ETA: 0s - loss: 0.6593 - mean_squared_error: 0.60 - 0s 831us/step - loss: 0.5632 - mean_squared_error: 0.5110 Epoch 93/500 12/12 [==============================] - 0s 831us/step - loss: 0.5634 - mean_squared_error: 0.5108 Epoch 94/500 12/12 [==============================] - 0s 914us/step - loss: 0.5685 - mean_squared_error: 0.5157 Epoch 95/500 12/12 [==============================] - 0s 831us/step - loss: 0.5348 - mean_squared_error: 0.4821 Epoch 96/500 12/12 [==============================] - 0s 831us/step - loss: 0.5352 - mean_squared_error: 0.4820 Epoch 97/500 12/12 [==============================] - 0s 914us/step - loss: 0.5495 - mean_squared_error: 0.4968 Epoch 98/500 12/12 [==============================] - 0s 831us/step - loss: 0.5339 - mean_squared_error: 0.4810 Epoch 99/500 12/12 [==============================] - 0s 914us/step - loss: 0.5374 - mean_squared_error: 0.4843 Epoch 100/500 12/12 [==============================] - 0s 914us/step - loss: 0.5442 - mean_squared_error: 0.4918 Epoch 101/500 12/12 [==============================] - 0s 831us/step - loss: 0.5456 - mean_squared_error: 0.4929 Epoch 102/500 12/12 [==============================] - 0s 914us/step - loss: 0.5389 - mean_squared_error: 0.4864 Epoch 103/500 12/12 [==============================] - 0s 997us/step - loss: 0.5271 - mean_squared_error: 0.4743 Epoch 104/500 12/12 [==============================] - 0s 914us/step - loss: 0.5325 - mean_squared_error: 0.4802 Epoch 105/500 12/12 [==============================] - 0s 997us/step - loss: 0.5231 - mean_squared_error: 0.4708 Epoch 106/500 12/12 [==============================] - 0s 997us/step - loss: 0.5379 - mean_squared_error: 0.4860 Epoch 107/500 12/12 [==============================] - 0s 997us/step - loss: 0.5249 - mean_squared_error: 0.4729 Epoch 108/500 12/12 [==============================] - 0s 997us/step - loss: 0.5214 - mean_squared_error: 0.4696 Epoch 109/500 12/12 [==============================] - 0s 914us/step - loss: 0.5206 - mean_squared_error: 0.4689 Epoch 110/500 12/12 [==============================] - 0s 914us/step - loss: 0.5276 - mean_squared_error: 0.4762 Epoch 111/500 12/12 [==============================] - 0s 997us/step - loss: 0.5240 - mean_squared_error: 0.4728 Epoch 112/500 12/12 [==============================] - 0s 914us/step - loss: 0.5165 - mean_squared_error: 0.4656 Epoch 113/500 12/12 [==============================] - 0s 1ms/step - loss: 0.5200 - mean_squared_error: 0.4690 Epoch 114/500 12/12 [==============================] - 0s 914us/step - loss: 0.5207 - mean_squared_error: 0.4697 Epoch 115/500 12/12 [==============================] - 0s 914us/step - loss: 0.5293 - mean_squared_error: 0.4786 Epoch 116/500 12/12 [==============================] - 0s 1ms/step - loss: 0.5106 - mean_squared_error: 0.4601 Epoch 117/500 12/12 [==============================] - 0s 997us/step - loss: 0.5071 - mean_squared_error: 0.4564 Epoch 118/500 12/12 [==============================] - 0s 997us/step - loss: 0.5180 - mean_squared_error: 0.4675 Epoch 119/500 12/12 [==============================] - 0s 997us/step - loss: 0.5087 - mean_squared_error: 0.4584 Epoch 120/500 12/12 [==============================] - 0s 997us/step - loss: 0.5141 - mean_squared_error: 0.4640 Epoch 121/500 12/12 [==============================] - 0s 997us/step - loss: 0.5059 - mean_squared_error: 0.4559 Epoch 122/500 12/12 [==============================] - 0s 997us/step - loss: 0.5209 - mean_squared_error: 0.4711 Epoch 123/500 12/12 [==============================] - 0s 1ms/step - loss: 0.5055 - mean_squared_error: 0.4558 Epoch 124/500 12/12 [==============================] - 0s 1ms/step - loss: 0.5116 - mean_squared_error: 0.4619 Epoch 125/500 12/12 [==============================] - 0s 914us/step - loss: 0.5177 - mean_squared_error: 0.4681 Epoch 126/500 12/12 [==============================] - 0s 914us/step - loss: 0.5169 - mean_squared_error: 0.4676 Epoch 127/500 12/12 [==============================] - 0s 914us/step - loss: 0.5237 - mean_squared_error: 0.4746 Epoch 128/500 12/12 [==============================] - 0s 914us/step - loss: 0.5234 - mean_squared_error: 0.4743 Epoch 129/500 12/12 [==============================] - 0s 914us/step - loss: 0.5121 - mean_squared_error: 0.4633 Epoch 130/500 12/12 [==============================] - 0s 831us/step - loss: 0.5121 - mean_squared_error: 0.4635 Epoch 131/500 12/12 [==============================] - 0s 914us/step - loss: 0.5044 - mean_squared_error: 0.4557 Epoch 132/500 12/12 [==============================] - 0s 914us/step - loss: 0.5217 - mean_squared_error: 0.4731 Epoch 133/500 12/12 [==============================] - 0s 914us/step - loss: 0.5032 - mean_squared_error: 0.4551 Epoch 134/500 12/12 [==============================] - 0s 831us/step - loss: 0.5157 - mean_squared_error: 0.4673 Epoch 135/500 12/12 [==============================] - 0s 914us/step - loss: 0.5170 - mean_squared_error: 0.4691 Epoch 136/500 12/12 [==============================] - 0s 914us/step - loss: 0.4969 - mean_squared_error: 0.4492 Epoch 137/500 12/12 [==============================] - 0s 831us/step - loss: 0.5029 - mean_squared_error: 0.4554 Epoch 138/500 12/12 [==============================] - 0s 914us/step - loss: 0.5109 - mean_squared_error: 0.4631 Epoch 139/500 12/12 [==============================] - 0s 914us/step - loss: 0.4966 - mean_squared_error: 0.4492 Epoch 140/500 12/12 [==============================] - 0s 831us/step - loss: 0.5093 - mean_squared_error: 0.4618 Epoch 141/500 12/12 [==============================] - 0s 831us/step - loss: 0.5121 - mean_squared_error: 0.4646 Epoch 142/500 12/12 [==============================] - 0s 914us/step - loss: 0.5020 - mean_squared_error: 0.4548 Epoch 143/500 12/12 [==============================] - 0s 831us/step - loss: 0.5026 - mean_squared_error: 0.4555 Epoch 144/500 12/12 [==============================] - 0s 914us/step - loss: 0.5054 - mean_squared_error: 0.4584 Epoch 145/500 12/12 [==============================] - 0s 997us/step - loss: 0.5097 - mean_squared_error: 0.4627 Epoch 146/500 12/12 [==============================] - 0s 1ms/step - loss: 0.5001 - mean_squared_error: 0.4536 Epoch 147/500 12/12 [==============================] - 0s 997us/step - loss: 0.5083 - mean_squared_error: 0.4620 Epoch 148/500 12/12 [==============================] - 0s 914us/step - loss: 0.5031 - mean_squared_error: 0.4567 Epoch 149/500 12/12 [==============================] - 0s 997us/step - loss: 0.4968 - mean_squared_error: 0.4509 Epoch 150/500 12/12 [==============================] - 0s 997us/step - loss: 0.5014 - mean_squared_error: 0.4554 Epoch 151/500 12/12 [==============================] - 0s 997us/step - loss: 0.4980 - mean_squared_error: 0.4522 Epoch 152/500 12/12 [==============================] - 0s 914us/step - loss: 0.5169 - mean_squared_error: 0.4709 Epoch 153/500 12/12 [==============================] - 0s 997us/step - loss: 0.5169 - mean_squared_error: 0.4712 Epoch 154/500 12/12 [==============================] - 0s 997us/step - loss: 0.5010 - mean_squared_error: 0.4552 Epoch 155/500 12/12 [==============================] - 0s 997us/step - loss: 0.4949 - mean_squared_error: 0.4492 Epoch 156/500 12/12 [==============================] - 0s 997us/step - loss: 0.5052 - mean_squared_error: 0.4598 Epoch 157/500 12/12 [==============================] - 0s 914us/step - loss: 0.5030 - mean_squared_error: 0.4579 Epoch 158/500 12/12 [==============================] - 0s 997us/step - loss: 0.4938 - mean_squared_error: 0.4487 Epoch 159/500 12/12 [==============================] - 0s 997us/step - loss: 0.4946 - mean_squared_error: 0.4496 Epoch 160/500 12/12 [==============================] - 0s 997us/step - loss: 0.5011 - mean_squared_error: 0.4562 Epoch 161/500 12/12 [==============================] - 0s 997us/step - loss: 0.5103 - mean_squared_error: 0.4655 Epoch 162/500 12/12 [==============================] - 0s 914us/step - loss: 0.5038 - mean_squared_error: 0.4588 Epoch 163/500 12/12 [==============================] - 0s 914us/step - loss: 0.4986 - mean_squared_error: 0.4538 Epoch 164/500 12/12 [==============================] - 0s 997us/step - loss: 0.4928 - mean_squared_error: 0.4480 Epoch 165/500 12/12 [==============================] - 0s 997us/step - loss: 0.5066 - mean_squared_error: 0.4620 Epoch 166/500 12/12 [==============================] - 0s 997us/step - loss: 0.4858 - mean_squared_error: 0.4413 Epoch 167/500 12/12 [==============================] - 0s 997us/step - loss: 0.5000 - mean_squared_error: 0.4554 Epoch 168/500 12/12 [==============================] - 0s 831us/step - loss: 0.4996 - mean_squared_error: 0.4554 Epoch 169/500 12/12 [==============================] - 0s 914us/step - loss: 0.4975 - mean_squared_error: 0.4532 Epoch 170/500 12/12 [==============================] - 0s 831us/step - loss: 0.4909 - mean_squared_error: 0.4466 Epoch 171/500 12/12 [==============================] - 0s 914us/step - loss: 0.4951 - mean_squared_error: 0.4507 Epoch 172/500 12/12 [==============================] - 0s 914us/step - loss: 0.4942 - mean_squared_error: 0.4504 Epoch 173/500 12/12 [==============================] - 0s 914us/step - loss: 0.5016 - mean_squared_error: 0.4576 Epoch 174/500 12/12 [==============================] - 0s 914us/step - loss: 0.4892 - mean_squared_error: 0.4452 Epoch 175/500 12/12 [==============================] - 0s 914us/step - loss: 0.4948 - mean_squared_error: 0.4511 Epoch 176/500 12/12 [==============================] - 0s 914us/step - loss: 0.5082 - mean_squared_error: 0.4645 Epoch 177/500 12/12 [==============================] - 0s 914us/step - loss: 0.5085 - mean_squared_error: 0.4646 Epoch 178/500 12/12 [==============================] - 0s 831us/step - loss: 0.5006 - mean_squared_error: 0.4572 Epoch 179/500 12/12 [==============================] - 0s 914us/step - loss: 0.5077 - mean_squared_error: 0.4641 Epoch 180/500 12/12 [==============================] - 0s 914us/step - loss: 0.5040 - mean_squared_error: 0.4607 Epoch 181/500 12/12 [==============================] - 0s 831us/step - loss: 0.4861 - mean_squared_error: 0.4427 Epoch 182/500 12/12 [==============================] - 0s 997us/step - loss: 0.5036 - mean_squared_error: 0.4606 Epoch 183/500 12/12 [==============================] - 0s 831us/step - loss: 0.4977 - mean_squared_error: 0.4545 Epoch 184/500 12/12 [==============================] - 0s 831us/step - loss: 0.4888 - mean_squared_error: 0.4457 Epoch 185/500 12/12 [==============================] - 0s 914us/step - loss: 0.4947 - mean_squared_error: 0.4517 Epoch 186/500 12/12 [==============================] - 0s 914us/step - loss: 0.4958 - mean_squared_error: 0.4530 Epoch 187/500 12/12 [==============================] - 0s 997us/step - loss: 0.4928 - mean_squared_error: 0.4501 Epoch 188/500 12/12 [==============================] - 0s 997us/step - loss: 0.5002 - mean_squared_error: 0.4574 Epoch 189/500 12/12 [==============================] - 0s 997us/step - loss: 0.5088 - mean_squared_error: 0.4663 Epoch 190/500 12/12 [==============================] - 0s 997us/step - loss: 0.4980 - mean_squared_error: 0.4558 Epoch 191/500 12/12 [==============================] - 0s 997us/step - loss: 0.4959 - mean_squared_error: 0.4535 Epoch 192/500 12/12 [==============================] - 0s 997us/step - loss: 0.5028 - mean_squared_error: 0.4607 Epoch 193/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4911 - mean_squared_error: 0.4489 Epoch 194/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4980 - mean_squared_error: 0.4558 Epoch 195/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4962 - mean_squared_error: 0.4539 Epoch 196/500 12/12 [==============================] - 0s 997us/step - loss: 0.4887 - mean_squared_error: 0.4466 Epoch 197/500 12/12 [==============================] - 0s 997us/step - loss: 0.4911 - mean_squared_error: 0.4486 Epoch 198/500 12/12 [==============================] - 0s 914us/step - loss: 0.4886 - mean_squared_error: 0.4466 Epoch 199/500 12/12 [==============================] - 0s 997us/step - loss: 0.4933 - mean_squared_error: 0.4513 Epoch 200/500 12/12 [==============================] - 0s 997us/step - loss: 0.4768 - mean_squared_error: 0.4350 Epoch 201/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4939 - mean_squared_error: 0.4521 Epoch 202/500 12/12 [==============================] - 0s 914us/step - loss: 0.4848 - mean_squared_error: 0.4428 Epoch 203/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4992 - mean_squared_error: 0.4572 Epoch 204/500 12/12 [==============================] - 0s 997us/step - loss: 0.4936 - mean_squared_error: 0.4521 Epoch 205/500 12/12 [==============================] - 0s 997us/step - loss: 0.4865 - mean_squared_error: 0.4449 Epoch 206/500 12/12 [==============================] - 0s 997us/step - loss: 0.4902 - mean_squared_error: 0.4487 Epoch 207/500 12/12 [==============================] - 0s 997us/step - loss: 0.4857 - mean_squared_error: 0.4440 Epoch 208/500 12/12 [==============================] - 0s 914us/step - loss: 0.4905 - mean_squared_error: 0.4488 Epoch 209/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4914 - mean_squared_error: 0.4500 Epoch 210/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4826 - mean_squared_error: 0.4410 Epoch 211/500 12/12 [==============================] - 0s 831us/step - loss: 0.4852 - mean_squared_error: 0.4439 Epoch 212/500 12/12 [==============================] - 0s 914us/step - loss: 0.4881 - mean_squared_error: 0.4466 Epoch 213/500 12/12 [==============================] - 0s 914us/step - loss: 0.4860 - mean_squared_error: 0.4449 Epoch 214/500 12/12 [==============================] - 0s 831us/step - loss: 0.4865 - mean_squared_error: 0.4452 Epoch 215/500 12/12 [==============================] - 0s 831us/step - loss: 0.4871 - mean_squared_error: 0.4462 Epoch 216/500 12/12 [==============================] - 0s 914us/step - loss: 0.5099 - mean_squared_error: 0.4688 Epoch 217/500 12/12 [==============================] - 0s 997us/step - loss: 0.4843 - mean_squared_error: 0.4431 Epoch 218/500 12/12 [==============================] - 0s 831us/step - loss: 0.4968 - mean_squared_error: 0.4559 Epoch 219/500 12/12 [==============================] - 0s 914us/step - loss: 0.4801 - mean_squared_error: 0.4391 Epoch 220/500 12/12 [==============================] - 0s 831us/step - loss: 0.4926 - mean_squared_error: 0.4517 Epoch 221/500 12/12 [==============================] - 0s 914us/step - loss: 0.4865 - mean_squared_error: 0.4456 Epoch 222/500 12/12 [==============================] - 0s 914us/step - loss: 0.4888 - mean_squared_error: 0.4480 Epoch 223/500 12/12 [==============================] - 0s 914us/step - loss: 0.4878 - mean_squared_error: 0.4469 Epoch 224/500 12/12 [==============================] - 0s 914us/step - loss: 0.4942 - mean_squared_error: 0.4532 Epoch 225/500 12/12 [==============================] - 0s 831us/step - loss: 0.4958 - mean_squared_error: 0.4553 Epoch 226/500 12/12 [==============================] - 0s 914us/step - loss: 0.4748 - mean_squared_error: 0.4342 Epoch 227/500 12/12 [==============================] - 0s 831us/step - loss: 0.4878 - mean_squared_error: 0.4473 Epoch 228/500 12/12 [==============================] - 0s 831us/step - loss: 0.4783 - mean_squared_error: 0.4378 Epoch 229/500 12/12 [==============================] - 0s 997us/step - loss: 0.4882 - mean_squared_error: 0.4480 Epoch 230/500 12/12 [==============================] - 0s 997us/step - loss: 0.4852 - mean_squared_error: 0.4448 Epoch 231/500 12/12 [==============================] - 0s 914us/step - loss: 0.4745 - mean_squared_error: 0.4343 Epoch 232/500 12/12 [==============================] - 0s 914us/step - loss: 0.4791 - mean_squared_error: 0.4386 Epoch 233/500 12/12 [==============================] - 0s 914us/step - loss: 0.4898 - mean_squared_error: 0.4495 Epoch 234/500 12/12 [==============================] - 0s 914us/step - loss: 0.4804 - mean_squared_error: 0.4400 Epoch 235/500 12/12 [==============================] - 0s 914us/step - loss: 0.4910 - mean_squared_error: 0.4508 Epoch 236/500 12/12 [==============================] - 0s 997us/step - loss: 0.4835 - mean_squared_error: 0.4436 Epoch 237/500 12/12 [==============================] - 0s 997us/step - loss: 0.4896 - mean_squared_error: 0.4493 Epoch 238/500 12/12 [==============================] - 0s 914us/step - loss: 0.4823 - mean_squared_error: 0.4422 Epoch 239/500 12/12 [==============================] - 0s 914us/step - loss: 0.4881 - mean_squared_error: 0.4481 Epoch 240/500 12/12 [==============================] - 0s 914us/step - loss: 0.4864 - mean_squared_error: 0.4465 Epoch 241/500 12/12 [==============================] - 0s 997us/step - loss: 0.4928 - mean_squared_error: 0.4531 Epoch 242/500 12/12 [==============================] - 0s 914us/step - loss: 0.4710 - mean_squared_error: 0.4314 Epoch 243/500 12/12 [==============================] - 0s 997us/step - loss: 0.4747 - mean_squared_error: 0.4348 Epoch 244/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4831 - mean_squared_error: 0.4430 Epoch 245/500 12/12 [==============================] - 0s 997us/step - loss: 0.4906 - mean_squared_error: 0.4506 Epoch 246/500 12/12 [==============================] - 0s 997us/step - loss: 0.4843 - mean_squared_error: 0.4445 Epoch 247/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4895 - mean_squared_error: 0.4498 Epoch 248/500 12/12 [==============================] - 0s 914us/step - loss: 0.4957 - mean_squared_error: 0.4560 Epoch 249/500 12/12 [==============================] - 0s 914us/step - loss: 0.4790 - mean_squared_error: 0.4392 Epoch 250/500 12/12 [==============================] - 0s 997us/step - loss: 0.4842 - mean_squared_error: 0.4443 Epoch 251/500 12/12 [==============================] - 0s 997us/step - loss: 0.4876 - mean_squared_error: 0.4480 Epoch 252/500 12/12 [==============================] - 0s 914us/step - loss: 0.4766 - mean_squared_error: 0.4369 Epoch 253/500 12/12 [==============================] - 0s 914us/step - loss: 0.4882 - mean_squared_error: 0.4488 Epoch 254/500 12/12 [==============================] - 0s 914us/step - loss: 0.4815 - mean_squared_error: 0.4421 Epoch 255/500 12/12 [==============================] - 0s 914us/step - loss: 0.4794 - mean_squared_error: 0.4399 Epoch 256/500 12/12 [==============================] - 0s 831us/step - loss: 0.4768 - mean_squared_error: 0.4374 Epoch 257/500 12/12 [==============================] - 0s 914us/step - loss: 0.4917 - mean_squared_error: 0.4522 Epoch 258/500 12/12 [==============================] - 0s 914us/step - loss: 0.4903 - mean_squared_error: 0.4511 Epoch 259/500 12/12 [==============================] - 0s 914us/step - loss: 0.4902 - mean_squared_error: 0.4511 Epoch 260/500 12/12 [==============================] - 0s 914us/step - loss: 0.4765 - mean_squared_error: 0.4373 Epoch 261/500 12/12 [==============================] - 0s 914us/step - loss: 0.5003 - mean_squared_error: 0.4615 Epoch 262/500 12/12 [==============================] - 0s 914us/step - loss: 0.4949 - mean_squared_error: 0.4558 Epoch 263/500 12/12 [==============================] - 0s 997us/step - loss: 0.4933 - mean_squared_error: 0.4542 Epoch 264/500 12/12 [==============================] - 0s 997us/step - loss: 0.4750 - mean_squared_error: 0.4360 Epoch 265/500 12/12 [==============================] - 0s 914us/step - loss: 0.4930 - mean_squared_error: 0.4540 Epoch 266/500 12/12 [==============================] - 0s 831us/step - loss: 0.4702 - mean_squared_error: 0.4314 Epoch 267/500 12/12 [==============================] - 0s 914us/step - loss: 0.4789 - mean_squared_error: 0.4399 Epoch 268/500 12/12 [==============================] - 0s 831us/step - loss: 0.4773 - mean_squared_error: 0.4382 Epoch 269/500 12/12 [==============================] - 0s 831us/step - loss: 0.4750 - mean_squared_error: 0.4361 Epoch 270/500 12/12 [==============================] - 0s 914us/step - loss: 0.4781 - mean_squared_error: 0.4392 Epoch 271/500 12/12 [==============================] - 0s 831us/step - loss: 0.4874 - mean_squared_error: 0.4484 Epoch 272/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4901 - mean_squared_error: 0.4513 Epoch 273/500 12/12 [==============================] - 0s 997us/step - loss: 0.4717 - mean_squared_error: 0.4330 Epoch 274/500 12/12 [==============================] - 0s 997us/step - loss: 0.4794 - mean_squared_error: 0.4407 Epoch 275/500 12/12 [==============================] - 0s 997us/step - loss: 0.4723 - mean_squared_error: 0.4332 Epoch 276/500 12/12 [==============================] - 0s 997us/step - loss: 0.4840 - mean_squared_error: 0.4453 Epoch 277/500 12/12 [==============================] - 0s 914us/step - loss: 0.4767 - mean_squared_error: 0.4380 Epoch 278/500 12/12 [==============================] - 0s 997us/step - loss: 0.4690 - mean_squared_error: 0.4304 Epoch 279/500 12/12 [==============================] - 0s 997us/step - loss: 0.4920 - mean_squared_error: 0.4534 Epoch 280/500 12/12 [==============================] - 0s 914us/step - loss: 0.4745 - mean_squared_error: 0.4361 Epoch 281/500 12/12 [==============================] - 0s 997us/step - loss: 0.4793 - mean_squared_error: 0.4408 Epoch 282/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4738 - mean_squared_error: 0.4351 Epoch 283/500 12/12 [==============================] - 0s 997us/step - loss: 0.4729 - mean_squared_error: 0.4346 Epoch 284/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4732 - mean_squared_error: 0.4351 Epoch 285/500 12/12 [==============================] - 0s 914us/step - loss: 0.4814 - mean_squared_error: 0.4429 Epoch 286/500 12/12 [==============================] - 0s 997us/step - loss: 0.4776 - mean_squared_error: 0.4394 Epoch 287/500 12/12 [==============================] - 0s 997us/step - loss: 0.4759 - mean_squared_error: 0.4377 Epoch 288/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4846 - mean_squared_error: 0.4464 Epoch 289/500 12/12 [==============================] - 0s 997us/step - loss: 0.4806 - mean_squared_error: 0.4427 Epoch 290/500 12/12 [==============================] - 0s 997us/step - loss: 0.4740 - mean_squared_error: 0.4357 Epoch 291/500 12/12 [==============================] - 0s 914us/step - loss: 0.4869 - mean_squared_error: 0.4489 Epoch 292/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4670 - mean_squared_error: 0.4289 Epoch 293/500 12/12 [==============================] - 0s 914us/step - loss: 0.4663 - mean_squared_error: 0.4279 Epoch 294/500 12/12 [==============================] - 0s 997us/step - loss: 0.4858 - mean_squared_error: 0.4478 Epoch 295/500 12/12 [==============================] - ETA: 0s - loss: 0.4284 - mean_squared_error: 0.38 - 0s 997us/step - loss: 0.4856 - mean_squared_error: 0.4474 Epoch 296/500 12/12 [==============================] - 0s 914us/step - loss: 0.4715 - mean_squared_error: 0.4337 Epoch 297/500 12/12 [==============================] - 0s 914us/step - loss: 0.4877 - mean_squared_error: 0.4497 Epoch 298/500 12/12 [==============================] - 0s 914us/step - loss: 0.4744 - mean_squared_error: 0.4362 Epoch 299/500 12/12 [==============================] - 0s 914us/step - loss: 0.4915 - mean_squared_error: 0.4537 Epoch 300/500 12/12 [==============================] - 0s 914us/step - loss: 0.4784 - mean_squared_error: 0.4408 Epoch 301/500 12/12 [==============================] - 0s 831us/step - loss: 0.4872 - mean_squared_error: 0.4493 Epoch 302/500 12/12 [==============================] - 0s 831us/step - loss: 0.4858 - mean_squared_error: 0.4479 Epoch 303/500 12/12 [==============================] - 0s 831us/step - loss: 0.4737 - mean_squared_error: 0.4360 Epoch 304/500 12/12 [==============================] - 0s 914us/step - loss: 0.4818 - mean_squared_error: 0.4441 Epoch 305/500 12/12 [==============================] - 0s 831us/step - loss: 0.4703 - mean_squared_error: 0.4325 Epoch 306/500 12/12 [==============================] - 0s 831us/step - loss: 0.4749 - mean_squared_error: 0.4369 Epoch 307/500 12/12 [==============================] - 0s 831us/step - loss: 0.4825 - mean_squared_error: 0.4444 Epoch 308/500 12/12 [==============================] - 0s 831us/step - loss: 0.4876 - mean_squared_error: 0.4498 Epoch 309/500 12/12 [==============================] - 0s 914us/step - loss: 0.4893 - mean_squared_error: 0.4514 Epoch 310/500 12/12 [==============================] - 0s 831us/step - loss: 0.4757 - mean_squared_error: 0.4382 Epoch 311/500 12/12 [==============================] - 0s 914us/step - loss: 0.4786 - mean_squared_error: 0.4407 Epoch 312/500 12/12 [==============================] - 0s 831us/step - loss: 0.4715 - mean_squared_error: 0.4336 Epoch 313/500 12/12 [==============================] - 0s 831us/step - loss: 0.4817 - mean_squared_error: 0.4442 Epoch 314/500 12/12 [==============================] - 0s 997us/step - loss: 0.4802 - mean_squared_error: 0.4427 Epoch 315/500 12/12 [==============================] - 0s 997us/step - loss: 0.4779 - mean_squared_error: 0.4403 Epoch 316/500 12/12 [==============================] - 0s 997us/step - loss: 0.4818 - mean_squared_error: 0.4442 Epoch 317/500 12/12 [==============================] - 0s 914us/step - loss: 0.4690 - mean_squared_error: 0.4313 Epoch 318/500 12/12 [==============================] - 0s 997us/step - loss: 0.4845 - mean_squared_error: 0.4470 Epoch 319/500 12/12 [==============================] - 0s 997us/step - loss: 0.4756 - mean_squared_error: 0.4384 Epoch 320/500 12/12 [==============================] - 0s 914us/step - loss: 0.4771 - mean_squared_error: 0.4396 Epoch 321/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4689 - mean_squared_error: 0.4316 Epoch 322/500 12/12 [==============================] - 0s 914us/step - loss: 0.4783 - mean_squared_error: 0.4409 Epoch 323/500 12/12 [==============================] - 0s 914us/step - loss: 0.4731 - mean_squared_error: 0.4357 Epoch 324/500 12/12 [==============================] - 0s 997us/step - loss: 0.4806 - mean_squared_error: 0.4432 Epoch 325/500 12/12 [==============================] - 0s 905us/step - loss: 0.4802 - mean_squared_error: 0.4428 Epoch 326/500 12/12 [==============================] - 0s 914us/step - loss: 0.4650 - mean_squared_error: 0.4276 Epoch 327/500 12/12 [==============================] - 0s 914us/step - loss: 0.4819 - mean_squared_error: 0.4446 Epoch 328/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4749 - mean_squared_error: 0.4376 Epoch 329/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4655 - mean_squared_error: 0.4281 Epoch 330/500 12/12 [==============================] - 0s 997us/step - loss: 0.4602 - mean_squared_error: 0.4231 Epoch 331/500 12/12 [==============================] - 0s 997us/step - loss: 0.4623 - mean_squared_error: 0.4250 Epoch 332/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4849 - mean_squared_error: 0.4478 Epoch 333/500 12/12 [==============================] - 0s 997us/step - loss: 0.4748 - mean_squared_error: 0.4376 Epoch 334/500 12/12 [==============================] - 0s 997us/step - loss: 0.4749 - mean_squared_error: 0.4377 Epoch 335/500 12/12 [==============================] - 0s 997us/step - loss: 0.4799 - mean_squared_error: 0.4430 Epoch 336/500 12/12 [==============================] - 0s 997us/step - loss: 0.4747 - mean_squared_error: 0.4375 Epoch 337/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4800 - mean_squared_error: 0.4428 Epoch 338/500 12/12 [==============================] - 0s 914us/step - loss: 0.4792 - mean_squared_error: 0.4422 Epoch 339/500 12/12 [==============================] - 0s 914us/step - loss: 0.4879 - mean_squared_error: 0.4512 Epoch 340/500 12/12 [==============================] - 0s 831us/step - loss: 0.4812 - mean_squared_error: 0.4444 Epoch 341/500 12/12 [==============================] - 0s 914us/step - loss: 0.4733 - mean_squared_error: 0.4362 Epoch 342/500 12/12 [==============================] - 0s 831us/step - loss: 0.4836 - mean_squared_error: 0.4465 Epoch 343/500 12/12 [==============================] - 0s 914us/step - loss: 0.4638 - mean_squared_error: 0.4269 Epoch 344/500 12/12 [==============================] - 0s 914us/step - loss: 0.4777 - mean_squared_error: 0.4407 Epoch 345/500 12/12 [==============================] - 0s 914us/step - loss: 0.4822 - mean_squared_error: 0.4452 Epoch 346/500 12/12 [==============================] - 0s 914us/step - loss: 0.4939 - mean_squared_error: 0.4570 Epoch 347/500 12/12 [==============================] - 0s 914us/step - loss: 0.4800 - mean_squared_error: 0.4433 Epoch 348/500 12/12 [==============================] - 0s 914us/step - loss: 0.4911 - mean_squared_error: 0.4544 Epoch 349/500 12/12 [==============================] - 0s 831us/step - loss: 0.4725 - mean_squared_error: 0.4357 Epoch 350/500 12/12 [==============================] - 0s 914us/step - loss: 0.4804 - mean_squared_error: 0.4436 Epoch 351/500 12/12 [==============================] - 0s 914us/step - loss: 0.4863 - mean_squared_error: 0.4494 Epoch 352/500 12/12 [==============================] - 0s 914us/step - loss: 0.4747 - mean_squared_error: 0.4382 Epoch 353/500 12/12 [==============================] - 0s 914us/step - loss: 0.4767 - mean_squared_error: 0.4397 Epoch 354/500 12/12 [==============================] - 0s 914us/step - loss: 0.4868 - mean_squared_error: 0.4499 Epoch 355/500 12/12 [==============================] - 0s 831us/step - loss: 0.4741 - mean_squared_error: 0.4370 Epoch 356/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4773 - mean_squared_error: 0.4402 Epoch 357/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4682 - mean_squared_error: 0.4312 Epoch 358/500 12/12 [==============================] - 0s 997us/step - loss: 0.4839 - mean_squared_error: 0.4469 Epoch 359/500 12/12 [==============================] - 0s 997us/step - loss: 0.4744 - mean_squared_error: 0.4373 Epoch 360/500 12/12 [==============================] - 0s 997us/step - loss: 0.4732 - mean_squared_error: 0.4363 Epoch 361/500 12/12 [==============================] - 0s 914us/step - loss: 0.4794 - mean_squared_error: 0.4425 Epoch 362/500 12/12 [==============================] - 0s 997us/step - loss: 0.4694 - mean_squared_error: 0.4325 Epoch 363/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4719 - mean_squared_error: 0.4351 Epoch 364/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4754 - mean_squared_error: 0.4390 Epoch 365/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4726 - mean_squared_error: 0.4359 Epoch 366/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4661 - mean_squared_error: 0.4295 Epoch 367/500 12/12 [==============================] - 0s 997us/step - loss: 0.4629 - mean_squared_error: 0.4259 Epoch 368/500 12/12 [==============================] - 0s 997us/step - loss: 0.4626 - mean_squared_error: 0.4257 Epoch 369/500 12/12 [==============================] - 0s 997us/step - loss: 0.4736 - mean_squared_error: 0.4369 Epoch 370/500 12/12 [==============================] - 0s 997us/step - loss: 0.4738 - mean_squared_error: 0.4370 Epoch 371/500 12/12 [==============================] - 0s 914us/step - loss: 0.4680 - mean_squared_error: 0.4313 Epoch 372/500 12/12 [==============================] - ETA: 0s - loss: 0.5377 - mean_squared_error: 0.50 - 0s 914us/step - loss: 0.4761 - mean_squared_error: 0.4395 Epoch 373/500 12/12 [==============================] - 0s 914us/step - loss: 0.4680 - mean_squared_error: 0.4314 Epoch 374/500 12/12 [==============================] - 0s 997us/step - loss: 0.4758 - mean_squared_error: 0.4393 Epoch 375/500 12/12 [==============================] - 0s 997us/step - loss: 0.4678 - mean_squared_error: 0.4312 Epoch 376/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4753 - mean_squared_error: 0.4386 Epoch 377/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4732 - mean_squared_error: 0.4366 Epoch 378/500 12/12 [==============================] - 0s 997us/step - loss: 0.4823 - mean_squared_error: 0.4457 Epoch 379/500 12/12 [==============================] - 0s 997us/step - loss: 0.4660 - mean_squared_error: 0.4298 Epoch 380/500 12/12 [==============================] - 0s 831us/step - loss: 0.4701 - mean_squared_error: 0.4336 Epoch 381/500 12/12 [==============================] - 0s 997us/step - loss: 0.4616 - mean_squared_error: 0.4250 Epoch 382/500 12/12 [==============================] - 0s 914us/step - loss: 0.4738 - mean_squared_error: 0.4373 Epoch 383/500 12/12 [==============================] - 0s 914us/step - loss: 0.4727 - mean_squared_error: 0.4363 Epoch 384/500 12/12 [==============================] - 0s 831us/step - loss: 0.4834 - mean_squared_error: 0.4471 Epoch 385/500 12/12 [==============================] - 0s 831us/step - loss: 0.4599 - mean_squared_error: 0.4237 Epoch 386/500 12/12 [==============================] - 0s 831us/step - loss: 0.4637 - mean_squared_error: 0.4273 Epoch 387/500 12/12 [==============================] - 0s 914us/step - loss: 0.4712 - mean_squared_error: 0.4347 Epoch 388/500 12/12 [==============================] - 0s 997us/step - loss: 0.4711 - mean_squared_error: 0.4345 Epoch 389/500 12/12 [==============================] - 0s 997us/step - loss: 0.4821 - mean_squared_error: 0.4454 Epoch 390/500 12/12 [==============================] - 0s 914us/step - loss: 0.4809 - mean_squared_error: 0.4444 Epoch 391/500 12/12 [==============================] - 0s 914us/step - loss: 0.4810 - mean_squared_error: 0.4446 Epoch 392/500 12/12 [==============================] - 0s 831us/step - loss: 0.4830 - mean_squared_error: 0.4469 Epoch 393/500 12/12 [==============================] - 0s 914us/step - loss: 0.4737 - mean_squared_error: 0.4374 Epoch 394/500 12/12 [==============================] - 0s 914us/step - loss: 0.4661 - mean_squared_error: 0.4296 Epoch 395/500 12/12 [==============================] - ETA: 0s - loss: 0.3981 - mean_squared_error: 0.36 - 0s 831us/step - loss: 0.4775 - mean_squared_error: 0.4410 Epoch 396/500 12/12 [==============================] - 0s 914us/step - loss: 0.4653 - mean_squared_error: 0.4291 Epoch 397/500 12/12 [==============================] - 0s 831us/step - loss: 0.4882 - mean_squared_error: 0.4518 Epoch 398/500 12/12 [==============================] - 0s 914us/step - loss: 0.4724 - mean_squared_error: 0.4360 Epoch 399/500 12/12 [==============================] - 0s 997us/step - loss: 0.4758 - mean_squared_error: 0.4394 Epoch 400/500 12/12 [==============================] - 0s 997us/step - loss: 0.4794 - mean_squared_error: 0.4432 Epoch 401/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4657 - mean_squared_error: 0.4295 Epoch 402/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4606 - mean_squared_error: 0.4243 Epoch 403/500 12/12 [==============================] - 0s 914us/step - loss: 0.4734 - mean_squared_error: 0.4374 Epoch 404/500 12/12 [==============================] - 0s 997us/step - loss: 0.4684 - mean_squared_error: 0.4322 Epoch 405/500 12/12 [==============================] - 0s 997us/step - loss: 0.4551 - mean_squared_error: 0.4187 Epoch 406/500 12/12 [==============================] - 0s 914us/step - loss: 0.4710 - mean_squared_error: 0.4348 Epoch 407/500 12/12 [==============================] - 0s 997us/step - loss: 0.4813 - mean_squared_error: 0.4451 Epoch 408/500 12/12 [==============================] - 0s 997us/step - loss: 0.4580 - mean_squared_error: 0.4218 Epoch 409/500 12/12 [==============================] - 0s 997us/step - loss: 0.4665 - mean_squared_error: 0.4303 Epoch 410/500 12/12 [==============================] - 0s 914us/step - loss: 0.4572 - mean_squared_error: 0.4209 Epoch 411/500 12/12 [==============================] - 0s 914us/step - loss: 0.4837 - mean_squared_error: 0.4475 Epoch 412/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4749 - mean_squared_error: 0.4387 Epoch 413/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4705 - mean_squared_error: 0.4344 Epoch 414/500 12/12 [==============================] - 0s 997us/step - loss: 0.4743 - mean_squared_error: 0.4382 Epoch 415/500 12/12 [==============================] - 0s 997us/step - loss: 0.4561 - mean_squared_error: 0.4201 Epoch 416/500 12/12 [==============================] - 0s 997us/step - loss: 0.4787 - mean_squared_error: 0.4426 Epoch 417/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4637 - mean_squared_error: 0.4277 Epoch 418/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4836 - mean_squared_error: 0.4476 Epoch 419/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4718 - mean_squared_error: 0.4356 Epoch 420/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4710 - mean_squared_error: 0.4349 Epoch 421/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4700 - mean_squared_error: 0.4339 Epoch 422/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4639 - mean_squared_error: 0.4276 Epoch 423/500 12/12 [==============================] - 0s 997us/step - loss: 0.4717 - mean_squared_error: 0.4355 Epoch 424/500 12/12 [==============================] - 0s 997us/step - loss: 0.4766 - mean_squared_error: 0.4405 Epoch 425/500 12/12 [==============================] - 0s 914us/step - loss: 0.4610 - mean_squared_error: 0.4248 Epoch 426/500 12/12 [==============================] - 0s 914us/step - loss: 0.4640 - mean_squared_error: 0.4276 Epoch 427/500 12/12 [==============================] - 0s 997us/step - loss: 0.4581 - mean_squared_error: 0.4219 Epoch 428/500 12/12 [==============================] - 0s 914us/step - loss: 0.4654 - mean_squared_error: 0.4292 Epoch 429/500 12/12 [==============================] - 0s 997us/step - loss: 0.4748 - mean_squared_error: 0.4386 Epoch 430/500 12/12 [==============================] - 0s 914us/step - loss: 0.4685 - mean_squared_error: 0.4323 Epoch 431/500 12/12 [==============================] - 0s 997us/step - loss: 0.4877 - mean_squared_error: 0.4514 Epoch 432/500 12/12 [==============================] - 0s 997us/step - loss: 0.4682 - mean_squared_error: 0.4321 Epoch 433/500 12/12 [==============================] - 0s 997us/step - loss: 0.4675 - mean_squared_error: 0.4312 Epoch 434/500 12/12 [==============================] - 0s 997us/step - loss: 0.4713 - mean_squared_error: 0.4352 Epoch 435/500 12/12 [==============================] - 0s 914us/step - loss: 0.4710 - mean_squared_error: 0.4350 Epoch 436/500 12/12 [==============================] - 0s 914us/step - loss: 0.4850 - mean_squared_error: 0.4491 Epoch 437/500 12/12 [==============================] - 0s 997us/step - loss: 0.4689 - mean_squared_error: 0.4328 Epoch 438/500 12/12 [==============================] - 0s 997us/step - loss: 0.4592 - mean_squared_error: 0.4231 Epoch 439/500 12/12 [==============================] - 0s 914us/step - loss: 0.4681 - mean_squared_error: 0.4318 Epoch 440/500 12/12 [==============================] - 0s 831us/step - loss: 0.4704 - mean_squared_error: 0.4344 Epoch 441/500 12/12 [==============================] - 0s 997us/step - loss: 0.4792 - mean_squared_error: 0.4432 Epoch 442/500 12/12 [==============================] - 0s 997us/step - loss: 0.4669 - mean_squared_error: 0.4308 Epoch 443/500 12/12 [==============================] - 0s 997us/step - loss: 0.4693 - mean_squared_error: 0.4335 Epoch 444/500 12/12 [==============================] - 0s 997us/step - loss: 0.4639 - mean_squared_error: 0.4280 Epoch 445/500 12/12 [==============================] - 0s 997us/step - loss: 0.4736 - mean_squared_error: 0.4376 Epoch 446/500 12/12 [==============================] - 0s 914us/step - loss: 0.4778 - mean_squared_error: 0.4416 Epoch 447/500 12/12 [==============================] - 0s 997us/step - loss: 0.4701 - mean_squared_error: 0.4342 Epoch 448/500 12/12 [==============================] - 0s 997us/step - loss: 0.4636 - mean_squared_error: 0.4277 Epoch 449/500 12/12 [==============================] - 0s 914us/step - loss: 0.4685 - mean_squared_error: 0.4325 Epoch 450/500 12/12 [==============================] - 0s 997us/step - loss: 0.4652 - mean_squared_error: 0.4292 Epoch 451/500 12/12 [==============================] - 0s 997us/step - loss: 0.4681 - mean_squared_error: 0.4320 Epoch 452/500 12/12 [==============================] - 0s 997us/step - loss: 0.4559 - mean_squared_error: 0.4198 Epoch 453/500 12/12 [==============================] - 0s 997us/step - loss: 0.4627 - mean_squared_error: 0.4267 Epoch 454/500 12/12 [==============================] - 0s 997us/step - loss: 0.4650 - mean_squared_error: 0.4292 Epoch 455/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4734 - mean_squared_error: 0.4374 Epoch 456/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4646 - mean_squared_error: 0.4287 Epoch 457/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4814 - mean_squared_error: 0.4457 Epoch 458/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4669 - mean_squared_error: 0.4311 Epoch 459/500 12/12 [==============================] - 0s 997us/step - loss: 0.4703 - mean_squared_error: 0.4344 Epoch 460/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4660 - mean_squared_error: 0.4299 Epoch 461/500 12/12 [==============================] - 0s 997us/step - loss: 0.4695 - mean_squared_error: 0.4336 Epoch 462/500 12/12 [==============================] - 0s 997us/step - loss: 0.4712 - mean_squared_error: 0.4353 Epoch 463/500 12/12 [==============================] - 0s 997us/step - loss: 0.4622 - mean_squared_error: 0.4265 Epoch 464/500 12/12 [==============================] - 0s 997us/step - loss: 0.4708 - mean_squared_error: 0.4351 Epoch 465/500 12/12 [==============================] - 0s 914us/step - loss: 0.4539 - mean_squared_error: 0.4181 Epoch 466/500 12/12 [==============================] - 0s 914us/step - loss: 0.4740 - mean_squared_error: 0.4385 Epoch 467/500 12/12 [==============================] - 0s 997us/step - loss: 0.4661 - mean_squared_error: 0.4304 Epoch 468/500 12/12 [==============================] - 0s 997us/step - loss: 0.4613 - mean_squared_error: 0.4257 Epoch 469/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4597 - mean_squared_error: 0.4239 Epoch 470/500 12/12 [==============================] - 0s 997us/step - loss: 0.4783 - mean_squared_error: 0.4426 Epoch 471/500 12/12 [==============================] - 0s 914us/step - loss: 0.4752 - mean_squared_error: 0.4392 Epoch 472/500 12/12 [==============================] - 0s 914us/step - loss: 0.4641 - mean_squared_error: 0.4282 Epoch 473/500 12/12 [==============================] - 0s 914us/step - loss: 0.4679 - mean_squared_error: 0.4323 Epoch 474/500 12/12 [==============================] - 0s 914us/step - loss: 0.4588 - mean_squared_error: 0.4230 Epoch 475/500 12/12 [==============================] - 0s 997us/step - loss: 0.4512 - mean_squared_error: 0.4151 Epoch 476/500 12/12 [==============================] - 0s 997us/step - loss: 0.4775 - mean_squared_error: 0.4411 Epoch 477/500 12/12 [==============================] - 0s 914us/step - loss: 0.4852 - mean_squared_error: 0.4492 Epoch 478/500 12/12 [==============================] - 0s 914us/step - loss: 0.4711 - mean_squared_error: 0.4353 Epoch 479/500 12/12 [==============================] - 0s 997us/step - loss: 0.4575 - mean_squared_error: 0.4217 Epoch 480/500 12/12 [==============================] - 0s 914us/step - loss: 0.4524 - mean_squared_error: 0.4163 Epoch 481/500 12/12 [==============================] - 0s 914us/step - loss: 0.4563 - mean_squared_error: 0.4204 Epoch 482/500 12/12 [==============================] - 0s 997us/step - loss: 0.4751 - mean_squared_error: 0.4392 Epoch 483/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4568 - mean_squared_error: 0.4210 Epoch 484/500 12/12 [==============================] - 0s 997us/step - loss: 0.4717 - mean_squared_error: 0.4358 Epoch 485/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4621 - mean_squared_error: 0.4262 Epoch 486/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4652 - mean_squared_error: 0.4294 Epoch 487/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4604 - mean_squared_error: 0.4247 Epoch 488/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4709 - mean_squared_error: 0.4351 Epoch 489/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4589 - mean_squared_error: 0.4229 Epoch 490/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4556 - mean_squared_error: 0.4196 Epoch 491/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4728 - mean_squared_error: 0.4368 Epoch 492/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4698 - mean_squared_error: 0.4341 Epoch 493/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4708 - mean_squared_error: 0.4349 Epoch 494/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4737 - mean_squared_error: 0.4377 Epoch 495/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4811 - mean_squared_error: 0.4452 Epoch 496/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4574 - mean_squared_error: 0.4217 Epoch 497/500 12/12 [==============================] - 0s 997us/step - loss: 0.4642 - mean_squared_error: 0.4285 Epoch 498/500 12/12 [==============================] - 0s 1ms/step - loss: 0.4582 - mean_squared_error: 0.4223 Epoch 499/500 12/12 [==============================] - 0s 997us/step - loss: 0.4771 - mean_squared_error: 0.4412 Epoch 500/500 12/12 [==============================] - 0s 997us/step - loss: 0.4618 - mean_squared_error: 0.4260 Total Time Taken is : -8.165160179138184
y_pred_reg_1=model_reg_1.predict(X_valid).astype("int64")
###########################################################
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
print("The Accuracy of the model is : ",accuracy_score(y_valid,y_pred_reg_1))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_valid,y_pred_reg_1),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.56
history=history_reg_1.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["mean_squared_error"])
ax.set_title("Mean Squred Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'mean_squared_error'])
Some Experimentation
We will drop some columns that show some redundancy and check if the model improves, and if so how. We will also try to do some PCA on the parameters to make them good.
X_train,X_test,y_train,y_test=train_test_split(X_train1,y_train1,test_size=0.30,random_state=0)
###################################################################
#Categorical Neural Network
###################################################################
model_cat_2=k.Sequential()
#model_cat_2.add(Flatten(input_shape=(X_train.shape[1],)))
#model_cat_2.add(Reshape((784,),input_shape=(X_train.shape[0],X_train.shape[1],)))
model_cat_2.add(BatchNormalization())
model_cat_2.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.2, input_shape=(60,)))
model_cat_2.add(Dense(30,activation="relu",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.2, input_shape=(30,)))
model_cat_2.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.4, input_shape=(60,)))
model_cat_2.add(Dense(60,activation="relu",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.2, input_shape=(60,)))
model_cat_2.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.4, input_shape=(60,)))
model_cat_2.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.2, input_shape=(30,)))
model_cat_2.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dropout(0.2, input_shape=(30,)))
model_cat_2.add(Dense(15,activation="sigmoid",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_2.add(Dense(9,activation="softmax"))
sgd = optimizers.SGD(lr = 0.01,momentum=0.3)
model_cat_2.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.CategoricalAccuracy())
t=time.time()
###################################################################
#
###################################################################
history_cat_2=model_cat_2.fit(X_train,k.utils.to_categorical(y_train),validation_data = (X_valid,k.utils.to_categorical(y_valid)),batch_size=100, epochs = 500, verbose = 1)
print("Total Time Taken is : ",t-time.time())
Epoch 1/500
WARNING:tensorflow:Layer batch_normalization_2 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
9/9 [==============================] - 0s 36ms/step - loss: 0.4806 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4793 - val_categorical_accuracy: 0.0000e+00
Epoch 2/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4786 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4773 - val_categorical_accuracy: 0.0000e+00
Epoch 3/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4766 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4752 - val_categorical_accuracy: 0.0000e+00
Epoch 4/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4745 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4732 - val_categorical_accuracy: 0.0000e+00
Epoch 5/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4725 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4712 - val_categorical_accuracy: 0.0000e+00
Epoch 6/500
9/9 [==============================] - 0s 5ms/step - loss: 0.4705 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4692 - val_categorical_accuracy: 0.0000e+00
Epoch 7/500
9/9 [==============================] - 0s 5ms/step - loss: 0.4685 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4672 - val_categorical_accuracy: 0.0000e+00
Epoch 8/500
9/9 [==============================] - 0s 5ms/step - loss: 0.4665 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4652 - val_categorical_accuracy: 0.0000e+00
Epoch 9/500
9/9 [==============================] - 0s 5ms/step - loss: 0.4645 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4632 - val_categorical_accuracy: 0.0000e+00
Epoch 10/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4625 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4612 - val_categorical_accuracy: 0.0000e+00
Epoch 11/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4606 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4593 - val_categorical_accuracy: 0.0000e+00
Epoch 12/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4586 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4573 - val_categorical_accuracy: 0.0000e+00
Epoch 13/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4567 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4554 - val_categorical_accuracy: 0.0000e+00
Epoch 14/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4547 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4535 - val_categorical_accuracy: 0.0000e+00
Epoch 15/500
9/9 [==============================] - 0s 6ms/step - loss: 0.4528 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4516 - val_categorical_accuracy: 0.0000e+00
Epoch 16/500
9/9 [==============================] - 0s 4ms/step - loss: 0.4509 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4496 - val_categorical_accuracy: 0.0000e+00
Epoch 17/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4490 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4477 - val_categorical_accuracy: 0.0000e+00
Epoch 18/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4471 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4459 - val_categorical_accuracy: 0.0000e+00
Epoch 19/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4452 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4440 - val_categorical_accuracy: 0.0000e+00
Epoch 20/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4433 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4421 - val_categorical_accuracy: 0.0000e+00
Epoch 21/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4414 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4402 - val_categorical_accuracy: 0.0000e+00
Epoch 22/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4396 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4384 - val_categorical_accuracy: 0.0000e+00
Epoch 23/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4378 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4365 - val_categorical_accuracy: 0.0000e+00
Epoch 24/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4359 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4347 - val_categorical_accuracy: 0.0000e+00
Epoch 25/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4341 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4329 - val_categorical_accuracy: 0.0000e+00
Epoch 26/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4322 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4311 - val_categorical_accuracy: 0.0000e+00
Epoch 27/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4304 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4292 - val_categorical_accuracy: 0.0000e+00
Epoch 28/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4286 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4274 - val_categorical_accuracy: 0.0000e+00
Epoch 29/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4268 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4256 - val_categorical_accuracy: 0.0000e+00
Epoch 30/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4250 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4239 - val_categorical_accuracy: 0.0000e+00
Epoch 31/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4232 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4221 - val_categorical_accuracy: 0.0000e+00
Epoch 32/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4215 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4203 - val_categorical_accuracy: 0.0000e+00
Epoch 33/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4197 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4186 - val_categorical_accuracy: 0.0000e+00
Epoch 34/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4179 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4168 - val_categorical_accuracy: 0.0000e+00
Epoch 35/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4162 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4151 - val_categorical_accuracy: 0.0000e+00
Epoch 36/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4145 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4133 - val_categorical_accuracy: 0.0000e+00
Epoch 37/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4127 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4116 - val_categorical_accuracy: 0.0000e+00
Epoch 38/500
9/9 [==============================] - 0s 2ms/step - loss: 0.4110 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4099 - val_categorical_accuracy: 0.0000e+00
Epoch 39/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4093 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4082 - val_categorical_accuracy: 0.0000e+00
Epoch 40/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4076 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4065 - val_categorical_accuracy: 0.0000e+00
Epoch 41/500
9/9 [==============================] - 0s 2ms/step - loss: 0.4059 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4048 - val_categorical_accuracy: 0.0000e+00
Epoch 42/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4042 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4031 - val_categorical_accuracy: 0.0000e+00
Epoch 43/500
9/9 [==============================] - 0s 2ms/step - loss: 0.4025 - categorical_accuracy: 0.0000e+00 - val_loss: 0.4014 - val_categorical_accuracy: 0.0000e+00
Epoch 44/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4008 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3998 - val_categorical_accuracy: 0.0000e+00
Epoch 45/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3992 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3981 - val_categorical_accuracy: 0.0000e+00
Epoch 46/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3975 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3964 - val_categorical_accuracy: 0.0000e+00
Epoch 47/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3958 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3948 - val_categorical_accuracy: 0.0000e+00
Epoch 48/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3942 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3932 - val_categorical_accuracy: 0.0000e+00
Epoch 49/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3926 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3915 - val_categorical_accuracy: 0.0000e+00
Epoch 50/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3909 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3899 - val_categorical_accuracy: 0.0000e+00
Epoch 51/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3893 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3883 - val_categorical_accuracy: 0.0000e+00
Epoch 52/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3877 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3867 - val_categorical_accuracy: 0.0000e+00
Epoch 53/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3861 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3851 - val_categorical_accuracy: 0.0000e+00
Epoch 54/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3845 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3835 - val_categorical_accuracy: 0.0000e+00
Epoch 55/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3829 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3819 - val_categorical_accuracy: 0.0000e+00
Epoch 56/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3814 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3803 - val_categorical_accuracy: 0.0000e+00
Epoch 57/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3798 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3788 - val_categorical_accuracy: 0.0000e+00
Epoch 58/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3782 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3772 - val_categorical_accuracy: 0.0000e+00
Epoch 59/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3767 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3757 - val_categorical_accuracy: 0.0000e+00
Epoch 60/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3751 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3741 - val_categorical_accuracy: 0.0000e+00
Epoch 61/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3735 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3726 - val_categorical_accuracy: 0.0000e+00
Epoch 62/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3720 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3710 - val_categorical_accuracy: 0.0000e+00
Epoch 63/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3705 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3695 - val_categorical_accuracy: 0.0000e+00
Epoch 64/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3690 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3680 - val_categorical_accuracy: 0.0000e+00
Epoch 65/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3674 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3665 - val_categorical_accuracy: 0.0000e+00
Epoch 66/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3659 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3650 - val_categorical_accuracy: 0.0000e+00
Epoch 67/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3644 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3635 - val_categorical_accuracy: 0.0000e+00
Epoch 68/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3629 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3620 - val_categorical_accuracy: 0.0000e+00
Epoch 69/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3615 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3605 - val_categorical_accuracy: 0.0000e+00
Epoch 70/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3600 - categorical_accuracy: 0.0000e+00 - val_loss: 0.3590 - val_categorical_accuracy: 0.0000e+00
Epoch 71/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3585 - categorical_accuracy: 0.0024 - val_loss: 0.3576 - val_categorical_accuracy: 0.0000e+00
Epoch 72/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3570 - categorical_accuracy: 0.0167 - val_loss: 0.3561 - val_categorical_accuracy: 0.0000e+00
Epoch 73/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3556 - categorical_accuracy: 0.0358 - val_loss: 0.3547 - val_categorical_accuracy: 0.0000e+00
Epoch 74/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3541 - categorical_accuracy: 0.0882 - val_loss: 0.3532 - val_categorical_accuracy: 0.0000e+00
Epoch 75/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3527 - categorical_accuracy: 0.1514 - val_loss: 0.3518 - val_categorical_accuracy: 0.0000e+00
Epoch 76/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3512 - categorical_accuracy: 0.2384 - val_loss: 0.3503 - val_categorical_accuracy: 0.4225
Epoch 77/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3498 - categorical_accuracy: 0.2765 - val_loss: 0.3489 - val_categorical_accuracy: 0.4225
Epoch 78/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3484 - categorical_accuracy: 0.3385 - val_loss: 0.3475 - val_categorical_accuracy: 0.4225
Epoch 79/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3470 - categorical_accuracy: 0.3802 - val_loss: 0.3461 - val_categorical_accuracy: 0.4225
Epoch 80/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3456 - categorical_accuracy: 0.4052 - val_loss: 0.3447 - val_categorical_accuracy: 0.4225
Epoch 81/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3442 - categorical_accuracy: 0.4172 - val_loss: 0.3433 - val_categorical_accuracy: 0.4225
Epoch 82/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3428 - categorical_accuracy: 0.4255 - val_loss: 0.3419 - val_categorical_accuracy: 0.4225
Epoch 83/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3414 - categorical_accuracy: 0.4315 - val_loss: 0.3405 - val_categorical_accuracy: 0.4225
Epoch 84/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3400 - categorical_accuracy: 0.4303 - val_loss: 0.3391 - val_categorical_accuracy: 0.4225
Epoch 85/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3386 - categorical_accuracy: 0.4315 - val_loss: 0.3377 - val_categorical_accuracy: 0.4225
Epoch 86/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3372 - categorical_accuracy: 0.4327 - val_loss: 0.3364 - val_categorical_accuracy: 0.4225
Epoch 87/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3359 - categorical_accuracy: 0.4327 - val_loss: 0.3350 - val_categorical_accuracy: 0.4225
Epoch 88/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3345 - categorical_accuracy: 0.4327 - val_loss: 0.3337 - val_categorical_accuracy: 0.4225
Epoch 89/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3332 - categorical_accuracy: 0.4327 - val_loss: 0.3323 - val_categorical_accuracy: 0.4225
Epoch 90/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3318 - categorical_accuracy: 0.4327 - val_loss: 0.3310 - val_categorical_accuracy: 0.4225
Epoch 91/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3305 - categorical_accuracy: 0.4327 - val_loss: 0.3297 - val_categorical_accuracy: 0.4225
Epoch 92/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3292 - categorical_accuracy: 0.4327 - val_loss: 0.3283 - val_categorical_accuracy: 0.4225
Epoch 93/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3278 - categorical_accuracy: 0.4327 - val_loss: 0.3270 - val_categorical_accuracy: 0.4225
Epoch 94/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3265 - categorical_accuracy: 0.4327 - val_loss: 0.3257 - val_categorical_accuracy: 0.4225
Epoch 95/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3252 - categorical_accuracy: 0.4327 - val_loss: 0.3244 - val_categorical_accuracy: 0.4225
Epoch 96/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3239 - categorical_accuracy: 0.4327 - val_loss: 0.3231 - val_categorical_accuracy: 0.4225
Epoch 97/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3226 - categorical_accuracy: 0.4327 - val_loss: 0.3218 - val_categorical_accuracy: 0.4225
Epoch 98/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3213 - categorical_accuracy: 0.4327 - val_loss: 0.3205 - val_categorical_accuracy: 0.4225
Epoch 99/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3200 - categorical_accuracy: 0.4327 - val_loss: 0.3192 - val_categorical_accuracy: 0.4225
Epoch 100/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3187 - categorical_accuracy: 0.4327 - val_loss: 0.3180 - val_categorical_accuracy: 0.4225
Epoch 101/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3175 - categorical_accuracy: 0.4327 - val_loss: 0.3167 - val_categorical_accuracy: 0.4225
Epoch 102/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3162 - categorical_accuracy: 0.4327 - val_loss: 0.3154 - val_categorical_accuracy: 0.4225
Epoch 103/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3150 - categorical_accuracy: 0.4327 - val_loss: 0.3142 - val_categorical_accuracy: 0.4225
Epoch 104/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3137 - categorical_accuracy: 0.4327 - val_loss: 0.3129 - val_categorical_accuracy: 0.4225
Epoch 105/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3125 - categorical_accuracy: 0.4327 - val_loss: 0.3117 - val_categorical_accuracy: 0.4225
Epoch 106/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3112 - categorical_accuracy: 0.4327 - val_loss: 0.3104 - val_categorical_accuracy: 0.4225
Epoch 107/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3100 - categorical_accuracy: 0.4327 - val_loss: 0.3092 - val_categorical_accuracy: 0.4225
Epoch 108/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3088 - categorical_accuracy: 0.4327 - val_loss: 0.3080 - val_categorical_accuracy: 0.4225
Epoch 109/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3075 - categorical_accuracy: 0.4327 - val_loss: 0.3068 - val_categorical_accuracy: 0.4225
Epoch 110/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3063 - categorical_accuracy: 0.4327 - val_loss: 0.3056 - val_categorical_accuracy: 0.4225
Epoch 111/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3051 - categorical_accuracy: 0.4327 - val_loss: 0.3044 - val_categorical_accuracy: 0.4225
Epoch 112/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3039 - categorical_accuracy: 0.4327 - val_loss: 0.3032 - val_categorical_accuracy: 0.4225
Epoch 113/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3027 - categorical_accuracy: 0.4327 - val_loss: 0.3020 - val_categorical_accuracy: 0.4225
Epoch 114/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3015 - categorical_accuracy: 0.4327 - val_loss: 0.3008 - val_categorical_accuracy: 0.4225
Epoch 115/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3003 - categorical_accuracy: 0.4327 - val_loss: 0.2996 - val_categorical_accuracy: 0.4225
Epoch 116/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2991 - categorical_accuracy: 0.4327 - val_loss: 0.2984 - val_categorical_accuracy: 0.4225
Epoch 117/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2980 - categorical_accuracy: 0.4327 - val_loss: 0.2972 - val_categorical_accuracy: 0.4225
Epoch 118/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2968 - categorical_accuracy: 0.4327 - val_loss: 0.2961 - val_categorical_accuracy: 0.4225
Epoch 119/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2956 - categorical_accuracy: 0.4327 - val_loss: 0.2949 - val_categorical_accuracy: 0.4225
Epoch 120/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2945 - categorical_accuracy: 0.4327 - val_loss: 0.2938 - val_categorical_accuracy: 0.4225
Epoch 121/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2933 - categorical_accuracy: 0.4327 - val_loss: 0.2926 - val_categorical_accuracy: 0.4225
Epoch 122/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2922 - categorical_accuracy: 0.4327 - val_loss: 0.2915 - val_categorical_accuracy: 0.4225
Epoch 123/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2910 - categorical_accuracy: 0.4327 - val_loss: 0.2903 - val_categorical_accuracy: 0.4225
Epoch 124/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2899 - categorical_accuracy: 0.4327 - val_loss: 0.2892 - val_categorical_accuracy: 0.4225
Epoch 125/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2888 - categorical_accuracy: 0.4327 - val_loss: 0.2881 - val_categorical_accuracy: 0.4225
Epoch 126/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2877 - categorical_accuracy: 0.4327 - val_loss: 0.2870 - val_categorical_accuracy: 0.4225
Epoch 127/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2865 - categorical_accuracy: 0.4327 - val_loss: 0.2859 - val_categorical_accuracy: 0.4225
Epoch 128/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2854 - categorical_accuracy: 0.4327 - val_loss: 0.2848 - val_categorical_accuracy: 0.4225
Epoch 129/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2843 - categorical_accuracy: 0.4327 - val_loss: 0.2837 - val_categorical_accuracy: 0.4225
Epoch 130/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2832 - categorical_accuracy: 0.4327 - val_loss: 0.2826 - val_categorical_accuracy: 0.4225
Epoch 131/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2821 - categorical_accuracy: 0.4327 - val_loss: 0.2815 - val_categorical_accuracy: 0.4225
Epoch 132/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2811 - categorical_accuracy: 0.4327 - val_loss: 0.2804 - val_categorical_accuracy: 0.4225
Epoch 133/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2800 - categorical_accuracy: 0.4327 - val_loss: 0.2793 - val_categorical_accuracy: 0.4225
Epoch 134/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2789 - categorical_accuracy: 0.4327 - val_loss: 0.2783 - val_categorical_accuracy: 0.4225
Epoch 135/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2778 - categorical_accuracy: 0.4327 - val_loss: 0.2772 - val_categorical_accuracy: 0.4225
Epoch 136/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2767 - categorical_accuracy: 0.4327 - val_loss: 0.2761 - val_categorical_accuracy: 0.4225
Epoch 137/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2757 - categorical_accuracy: 0.4327 - val_loss: 0.2751 - val_categorical_accuracy: 0.4225
Epoch 138/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2746 - categorical_accuracy: 0.4327 - val_loss: 0.2740 - val_categorical_accuracy: 0.4225
Epoch 139/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2736 - categorical_accuracy: 0.4327 - val_loss: 0.2730 - val_categorical_accuracy: 0.4225
Epoch 140/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2725 - categorical_accuracy: 0.4327 - val_loss: 0.2719 - val_categorical_accuracy: 0.4225
Epoch 141/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2715 - categorical_accuracy: 0.4327 - val_loss: 0.2709 - val_categorical_accuracy: 0.4225
Epoch 142/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2705 - categorical_accuracy: 0.4327 - val_loss: 0.2699 - val_categorical_accuracy: 0.4225
Epoch 143/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2695 - categorical_accuracy: 0.4327 - val_loss: 0.2689 - val_categorical_accuracy: 0.4225
Epoch 144/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2684 - categorical_accuracy: 0.4327 - val_loss: 0.2679 - val_categorical_accuracy: 0.4225
Epoch 145/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2674 - categorical_accuracy: 0.4327 - val_loss: 0.2668 - val_categorical_accuracy: 0.4225
Epoch 146/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2664 - categorical_accuracy: 0.4327 - val_loss: 0.2658 - val_categorical_accuracy: 0.4225
Epoch 147/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2655 - categorical_accuracy: 0.4327 - val_loss: 0.2648 - val_categorical_accuracy: 0.4225
Epoch 148/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2644 - categorical_accuracy: 0.4327 - val_loss: 0.2638 - val_categorical_accuracy: 0.4225
Epoch 149/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2634 - categorical_accuracy: 0.4327 - val_loss: 0.2629 - val_categorical_accuracy: 0.4225
Epoch 150/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2624 - categorical_accuracy: 0.4327 - val_loss: 0.2619 - val_categorical_accuracy: 0.4225
Epoch 151/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2615 - categorical_accuracy: 0.4327 - val_loss: 0.2609 - val_categorical_accuracy: 0.4225
Epoch 152/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2605 - categorical_accuracy: 0.4327 - val_loss: 0.2599 - val_categorical_accuracy: 0.4225
Epoch 153/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2595 - categorical_accuracy: 0.4327 - val_loss: 0.2590 - val_categorical_accuracy: 0.4225
Epoch 154/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2585 - categorical_accuracy: 0.4327 - val_loss: 0.2580 - val_categorical_accuracy: 0.4225
Epoch 155/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2576 - categorical_accuracy: 0.4327 - val_loss: 0.2570 - val_categorical_accuracy: 0.4225
Epoch 156/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2566 - categorical_accuracy: 0.4327 - val_loss: 0.2561 - val_categorical_accuracy: 0.4225
Epoch 157/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2557 - categorical_accuracy: 0.4327 - val_loss: 0.2551 - val_categorical_accuracy: 0.4225
Epoch 158/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2547 - categorical_accuracy: 0.4327 - val_loss: 0.2542 - val_categorical_accuracy: 0.4225
Epoch 159/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2538 - categorical_accuracy: 0.4327 - val_loss: 0.2533 - val_categorical_accuracy: 0.4225
Epoch 160/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2529 - categorical_accuracy: 0.4327 - val_loss: 0.2523 - val_categorical_accuracy: 0.4225
Epoch 161/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2519 - categorical_accuracy: 0.4327 - val_loss: 0.2514 - val_categorical_accuracy: 0.4225
Epoch 162/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2510 - categorical_accuracy: 0.4327 - val_loss: 0.2505 - val_categorical_accuracy: 0.4225
Epoch 163/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2501 - categorical_accuracy: 0.4327 - val_loss: 0.2496 - val_categorical_accuracy: 0.4225
Epoch 164/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2492 - categorical_accuracy: 0.4327 - val_loss: 0.2486 - val_categorical_accuracy: 0.4225
Epoch 165/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2483 - categorical_accuracy: 0.4327 - val_loss: 0.2477 - val_categorical_accuracy: 0.4225
Epoch 166/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2474 - categorical_accuracy: 0.4327 - val_loss: 0.2468 - val_categorical_accuracy: 0.4225
Epoch 167/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2465 - categorical_accuracy: 0.4327 - val_loss: 0.2459 - val_categorical_accuracy: 0.4225
Epoch 168/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2456 - categorical_accuracy: 0.4327 - val_loss: 0.2451 - val_categorical_accuracy: 0.4225
Epoch 169/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2446 - categorical_accuracy: 0.4327 - val_loss: 0.2442 - val_categorical_accuracy: 0.4225
Epoch 170/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2438 - categorical_accuracy: 0.4327 - val_loss: 0.2433 - val_categorical_accuracy: 0.4225
Epoch 171/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2429 - categorical_accuracy: 0.4327 - val_loss: 0.2424 - val_categorical_accuracy: 0.4225
Epoch 172/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2420 - categorical_accuracy: 0.4327 - val_loss: 0.2415 - val_categorical_accuracy: 0.4225
Epoch 173/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2412 - categorical_accuracy: 0.4327 - val_loss: 0.2407 - val_categorical_accuracy: 0.4225
Epoch 174/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2403 - categorical_accuracy: 0.4327 - val_loss: 0.2398 - val_categorical_accuracy: 0.4225
Epoch 175/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2394 - categorical_accuracy: 0.4327 - val_loss: 0.2389 - val_categorical_accuracy: 0.4225
Epoch 176/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2386 - categorical_accuracy: 0.4327 - val_loss: 0.2381 - val_categorical_accuracy: 0.4225
Epoch 177/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2377 - categorical_accuracy: 0.4327 - val_loss: 0.2372 - val_categorical_accuracy: 0.4225
Epoch 178/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2369 - categorical_accuracy: 0.4327 - val_loss: 0.2364 - val_categorical_accuracy: 0.4225
Epoch 179/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2360 - categorical_accuracy: 0.4327 - val_loss: 0.2355 - val_categorical_accuracy: 0.4225
Epoch 180/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2352 - categorical_accuracy: 0.4327 - val_loss: 0.2347 - val_categorical_accuracy: 0.4225
Epoch 181/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2344 - categorical_accuracy: 0.4327 - val_loss: 0.2339 - val_categorical_accuracy: 0.4225
Epoch 182/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2335 - categorical_accuracy: 0.4327 - val_loss: 0.2330 - val_categorical_accuracy: 0.4225
Epoch 183/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2327 - categorical_accuracy: 0.4327 - val_loss: 0.2322 - val_categorical_accuracy: 0.4225
Epoch 184/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2319 - categorical_accuracy: 0.4327 - val_loss: 0.2314 - val_categorical_accuracy: 0.4225
Epoch 185/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2311 - categorical_accuracy: 0.4327 - val_loss: 0.2306 - val_categorical_accuracy: 0.4225
Epoch 186/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2303 - categorical_accuracy: 0.4327 - val_loss: 0.2298 - val_categorical_accuracy: 0.4225
Epoch 187/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2295 - categorical_accuracy: 0.4327 - val_loss: 0.2290 - val_categorical_accuracy: 0.4225
Epoch 188/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2286 - categorical_accuracy: 0.4327 - val_loss: 0.2282 - val_categorical_accuracy: 0.4225
Epoch 189/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2278 - categorical_accuracy: 0.4327 - val_loss: 0.2274 - val_categorical_accuracy: 0.4225
Epoch 190/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2271 - categorical_accuracy: 0.4327 - val_loss: 0.2266 - val_categorical_accuracy: 0.4225
Epoch 191/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2263 - categorical_accuracy: 0.4327 - val_loss: 0.2258 - val_categorical_accuracy: 0.4225
Epoch 192/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2255 - categorical_accuracy: 0.4327 - val_loss: 0.2250 - val_categorical_accuracy: 0.4225
Epoch 193/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2247 - categorical_accuracy: 0.4327 - val_loss: 0.2242 - val_categorical_accuracy: 0.4225
Epoch 194/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2239 - categorical_accuracy: 0.4327 - val_loss: 0.2235 - val_categorical_accuracy: 0.4225
Epoch 195/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2232 - categorical_accuracy: 0.4327 - val_loss: 0.2227 - val_categorical_accuracy: 0.4225
Epoch 196/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2223 - categorical_accuracy: 0.4327 - val_loss: 0.2219 - val_categorical_accuracy: 0.4225
Epoch 197/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2216 - categorical_accuracy: 0.4327 - val_loss: 0.2212 - val_categorical_accuracy: 0.4225
Epoch 198/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2208 - categorical_accuracy: 0.4327 - val_loss: 0.2204 - val_categorical_accuracy: 0.4225
Epoch 199/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2201 - categorical_accuracy: 0.4327 - val_loss: 0.2196 - val_categorical_accuracy: 0.4225
Epoch 200/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2193 - categorical_accuracy: 0.4327 - val_loss: 0.2189 - val_categorical_accuracy: 0.4225
Epoch 201/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2186 - categorical_accuracy: 0.4327 - val_loss: 0.2181 - val_categorical_accuracy: 0.4225
Epoch 202/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2178 - categorical_accuracy: 0.4327 - val_loss: 0.2174 - val_categorical_accuracy: 0.4225
Epoch 203/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2171 - categorical_accuracy: 0.4327 - val_loss: 0.2167 - val_categorical_accuracy: 0.4225
Epoch 204/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2163 - categorical_accuracy: 0.4327 - val_loss: 0.2159 - val_categorical_accuracy: 0.4225
Epoch 205/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2156 - categorical_accuracy: 0.4327 - val_loss: 0.2152 - val_categorical_accuracy: 0.4225
Epoch 206/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2149 - categorical_accuracy: 0.4327 - val_loss: 0.2145 - val_categorical_accuracy: 0.4225
Epoch 207/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2141 - categorical_accuracy: 0.4327 - val_loss: 0.2137 - val_categorical_accuracy: 0.4225
Epoch 208/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2135 - categorical_accuracy: 0.4327 - val_loss: 0.2130 - val_categorical_accuracy: 0.4225
Epoch 209/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2127 - categorical_accuracy: 0.4327 - val_loss: 0.2123 - val_categorical_accuracy: 0.4225
Epoch 210/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2120 - categorical_accuracy: 0.4327 - val_loss: 0.2116 - val_categorical_accuracy: 0.4225
Epoch 211/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2113 - categorical_accuracy: 0.4327 - val_loss: 0.2109 - val_categorical_accuracy: 0.4225
Epoch 212/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2106 - categorical_accuracy: 0.4327 - val_loss: 0.2102 - val_categorical_accuracy: 0.4225
Epoch 213/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2099 - categorical_accuracy: 0.4327 - val_loss: 0.2095 - val_categorical_accuracy: 0.4225
Epoch 214/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2092 - categorical_accuracy: 0.4327 - val_loss: 0.2088 - val_categorical_accuracy: 0.4225
Epoch 215/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2085 - categorical_accuracy: 0.4327 - val_loss: 0.2081 - val_categorical_accuracy: 0.4225
Epoch 216/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2078 - categorical_accuracy: 0.4327 - val_loss: 0.2074 - val_categorical_accuracy: 0.4225
Epoch 217/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2071 - categorical_accuracy: 0.4327 - val_loss: 0.2067 - val_categorical_accuracy: 0.4225
Epoch 218/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2064 - categorical_accuracy: 0.4327 - val_loss: 0.2060 - val_categorical_accuracy: 0.4225
Epoch 219/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2058 - categorical_accuracy: 0.4327 - val_loss: 0.2053 - val_categorical_accuracy: 0.4225
Epoch 220/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2051 - categorical_accuracy: 0.4327 - val_loss: 0.2046 - val_categorical_accuracy: 0.4225
Epoch 221/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2044 - categorical_accuracy: 0.4327 - val_loss: 0.2040 - val_categorical_accuracy: 0.4225
Epoch 222/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2037 - categorical_accuracy: 0.4327 - val_loss: 0.2033 - val_categorical_accuracy: 0.4225
Epoch 223/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2031 - categorical_accuracy: 0.4327 - val_loss: 0.2026 - val_categorical_accuracy: 0.4225
Epoch 224/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2024 - categorical_accuracy: 0.4327 - val_loss: 0.2020 - val_categorical_accuracy: 0.4225
Epoch 225/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2018 - categorical_accuracy: 0.4327 - val_loss: 0.2013 - val_categorical_accuracy: 0.4225
Epoch 226/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2011 - categorical_accuracy: 0.4327 - val_loss: 0.2007 - val_categorical_accuracy: 0.4225
Epoch 227/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2004 - categorical_accuracy: 0.4327 - val_loss: 0.2000 - val_categorical_accuracy: 0.4225
Epoch 228/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1998 - categorical_accuracy: 0.4327 - val_loss: 0.1994 - val_categorical_accuracy: 0.4225
Epoch 229/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1991 - categorical_accuracy: 0.4327 - val_loss: 0.1987 - val_categorical_accuracy: 0.4225
Epoch 230/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1985 - categorical_accuracy: 0.4327 - val_loss: 0.1981 - val_categorical_accuracy: 0.4225
Epoch 231/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1979 - categorical_accuracy: 0.4327 - val_loss: 0.1974 - val_categorical_accuracy: 0.4225
Epoch 232/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1972 - categorical_accuracy: 0.4327 - val_loss: 0.1968 - val_categorical_accuracy: 0.4225
Epoch 233/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1966 - categorical_accuracy: 0.4327 - val_loss: 0.1962 - val_categorical_accuracy: 0.4225
Epoch 234/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1960 - categorical_accuracy: 0.4327 - val_loss: 0.1955 - val_categorical_accuracy: 0.4225
Epoch 235/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1953 - categorical_accuracy: 0.4327 - val_loss: 0.1949 - val_categorical_accuracy: 0.4225
Epoch 236/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1947 - categorical_accuracy: 0.4327 - val_loss: 0.1943 - val_categorical_accuracy: 0.4225
Epoch 237/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1941 - categorical_accuracy: 0.4327 - val_loss: 0.1936 - val_categorical_accuracy: 0.4225
Epoch 238/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1935 - categorical_accuracy: 0.4327 - val_loss: 0.1930 - val_categorical_accuracy: 0.4225
Epoch 239/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1929 - categorical_accuracy: 0.4327 - val_loss: 0.1924 - val_categorical_accuracy: 0.4225
Epoch 240/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1922 - categorical_accuracy: 0.4327 - val_loss: 0.1918 - val_categorical_accuracy: 0.4225
Epoch 241/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1916 - categorical_accuracy: 0.4327 - val_loss: 0.1912 - val_categorical_accuracy: 0.4225
Epoch 242/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1911 - categorical_accuracy: 0.4327 - val_loss: 0.1906 - val_categorical_accuracy: 0.4225
Epoch 243/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1905 - categorical_accuracy: 0.4327 - val_loss: 0.1900 - val_categorical_accuracy: 0.4225
Epoch 244/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1898 - categorical_accuracy: 0.4327 - val_loss: 0.1894 - val_categorical_accuracy: 0.4225
Epoch 245/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1892 - categorical_accuracy: 0.4327 - val_loss: 0.1888 - val_categorical_accuracy: 0.4225
Epoch 246/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1886 - categorical_accuracy: 0.4327 - val_loss: 0.1882 - val_categorical_accuracy: 0.4225
Epoch 247/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1881 - categorical_accuracy: 0.4327 - val_loss: 0.1876 - val_categorical_accuracy: 0.4225
Epoch 248/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1875 - categorical_accuracy: 0.4327 - val_loss: 0.1870 - val_categorical_accuracy: 0.4225
Epoch 249/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1869 - categorical_accuracy: 0.4327 - val_loss: 0.1864 - val_categorical_accuracy: 0.4225
Epoch 250/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1863 - categorical_accuracy: 0.4327 - val_loss: 0.1859 - val_categorical_accuracy: 0.4225
Epoch 251/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1857 - categorical_accuracy: 0.4327 - val_loss: 0.1853 - val_categorical_accuracy: 0.4225
Epoch 252/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1852 - categorical_accuracy: 0.4327 - val_loss: 0.1847 - val_categorical_accuracy: 0.4225
Epoch 253/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1846 - categorical_accuracy: 0.4327 - val_loss: 0.1841 - val_categorical_accuracy: 0.4225
Epoch 254/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1840 - categorical_accuracy: 0.4327 - val_loss: 0.1836 - val_categorical_accuracy: 0.4225
Epoch 255/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1835 - categorical_accuracy: 0.4327 - val_loss: 0.1830 - val_categorical_accuracy: 0.4225
Epoch 256/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1829 - categorical_accuracy: 0.4327 - val_loss: 0.1824 - val_categorical_accuracy: 0.4225
Epoch 257/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1824 - categorical_accuracy: 0.4327 - val_loss: 0.1819 - val_categorical_accuracy: 0.4225
Epoch 258/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1817 - categorical_accuracy: 0.4327 - val_loss: 0.1813 - val_categorical_accuracy: 0.4225
Epoch 259/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1812 - categorical_accuracy: 0.4327 - val_loss: 0.1807 - val_categorical_accuracy: 0.4225
Epoch 260/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1806 - categorical_accuracy: 0.4327 - val_loss: 0.1802 - val_categorical_accuracy: 0.4225
Epoch 261/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1801 - categorical_accuracy: 0.4327 - val_loss: 0.1796 - val_categorical_accuracy: 0.4225
Epoch 262/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1796 - categorical_accuracy: 0.4327 - val_loss: 0.1791 - val_categorical_accuracy: 0.4225
Epoch 263/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1790 - categorical_accuracy: 0.4327 - val_loss: 0.1785 - val_categorical_accuracy: 0.4225
Epoch 264/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1785 - categorical_accuracy: 0.4327 - val_loss: 0.1780 - val_categorical_accuracy: 0.4225
Epoch 265/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1779 - categorical_accuracy: 0.4327 - val_loss: 0.1775 - val_categorical_accuracy: 0.4225
Epoch 266/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1774 - categorical_accuracy: 0.4327 - val_loss: 0.1769 - val_categorical_accuracy: 0.4225
Epoch 267/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1769 - categorical_accuracy: 0.4327 - val_loss: 0.1764 - val_categorical_accuracy: 0.4225
Epoch 268/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1763 - categorical_accuracy: 0.4327 - val_loss: 0.1758 - val_categorical_accuracy: 0.4225
Epoch 269/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1758 - categorical_accuracy: 0.4327 - val_loss: 0.1753 - val_categorical_accuracy: 0.4225
Epoch 270/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1753 - categorical_accuracy: 0.4327 - val_loss: 0.1748 - val_categorical_accuracy: 0.4225
Epoch 271/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1748 - categorical_accuracy: 0.4327 - val_loss: 0.1743 - val_categorical_accuracy: 0.4225
Epoch 272/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1742 - categorical_accuracy: 0.4327 - val_loss: 0.1737 - val_categorical_accuracy: 0.4225
Epoch 273/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1737 - categorical_accuracy: 0.4327 - val_loss: 0.1732 - val_categorical_accuracy: 0.4225
Epoch 274/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1732 - categorical_accuracy: 0.4327 - val_loss: 0.1727 - val_categorical_accuracy: 0.4225
Epoch 275/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1727 - categorical_accuracy: 0.4327 - val_loss: 0.1722 - val_categorical_accuracy: 0.4225
Epoch 276/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1722 - categorical_accuracy: 0.4327 - val_loss: 0.1717 - val_categorical_accuracy: 0.4225
Epoch 277/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1717 - categorical_accuracy: 0.4327 - val_loss: 0.1712 - val_categorical_accuracy: 0.4225
Epoch 278/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1712 - categorical_accuracy: 0.4327 - val_loss: 0.1707 - val_categorical_accuracy: 0.4225
Epoch 279/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1707 - categorical_accuracy: 0.4327 - val_loss: 0.1701 - val_categorical_accuracy: 0.4225
Epoch 280/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1702 - categorical_accuracy: 0.4327 - val_loss: 0.1696 - val_categorical_accuracy: 0.4225
Epoch 281/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1696 - categorical_accuracy: 0.4327 - val_loss: 0.1691 - val_categorical_accuracy: 0.4225
Epoch 282/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1692 - categorical_accuracy: 0.4327 - val_loss: 0.1686 - val_categorical_accuracy: 0.4225
Epoch 283/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1687 - categorical_accuracy: 0.4327 - val_loss: 0.1682 - val_categorical_accuracy: 0.4225
Epoch 284/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1682 - categorical_accuracy: 0.4327 - val_loss: 0.1677 - val_categorical_accuracy: 0.4225
Epoch 285/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1677 - categorical_accuracy: 0.4327 - val_loss: 0.1672 - val_categorical_accuracy: 0.4225
Epoch 286/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1672 - categorical_accuracy: 0.4327 - val_loss: 0.1667 - val_categorical_accuracy: 0.4225
Epoch 287/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1667 - categorical_accuracy: 0.4327 - val_loss: 0.1662 - val_categorical_accuracy: 0.4225
Epoch 288/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1663 - categorical_accuracy: 0.4327 - val_loss: 0.1657 - val_categorical_accuracy: 0.4225
Epoch 289/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1658 - categorical_accuracy: 0.4327 - val_loss: 0.1652 - val_categorical_accuracy: 0.4225
Epoch 290/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1653 - categorical_accuracy: 0.4327 - val_loss: 0.1647 - val_categorical_accuracy: 0.4225
Epoch 291/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1648 - categorical_accuracy: 0.4327 - val_loss: 0.1643 - val_categorical_accuracy: 0.4225
Epoch 292/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1644 - categorical_accuracy: 0.4327 - val_loss: 0.1638 - val_categorical_accuracy: 0.4225
Epoch 293/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1639 - categorical_accuracy: 0.4327 - val_loss: 0.1633 - val_categorical_accuracy: 0.4225
Epoch 294/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1634 - categorical_accuracy: 0.4327 - val_loss: 0.1628 - val_categorical_accuracy: 0.4225
Epoch 295/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1629 - categorical_accuracy: 0.4327 - val_loss: 0.1624 - val_categorical_accuracy: 0.4225
Epoch 296/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1625 - categorical_accuracy: 0.4327 - val_loss: 0.1619 - val_categorical_accuracy: 0.4225
Epoch 297/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1620 - categorical_accuracy: 0.4327 - val_loss: 0.1615 - val_categorical_accuracy: 0.4225
Epoch 298/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1616 - categorical_accuracy: 0.4327 - val_loss: 0.1610 - val_categorical_accuracy: 0.4225
Epoch 299/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1611 - categorical_accuracy: 0.4327 - val_loss: 0.1605 - val_categorical_accuracy: 0.4225
Epoch 300/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1607 - categorical_accuracy: 0.4327 - val_loss: 0.1601 - val_categorical_accuracy: 0.4225
Epoch 301/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1602 - categorical_accuracy: 0.4327 - val_loss: 0.1596 - val_categorical_accuracy: 0.4225
Epoch 302/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1598 - categorical_accuracy: 0.4327 - val_loss: 0.1592 - val_categorical_accuracy: 0.4225
Epoch 303/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1593 - categorical_accuracy: 0.4327 - val_loss: 0.1587 - val_categorical_accuracy: 0.4225
Epoch 304/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1589 - categorical_accuracy: 0.4327 - val_loss: 0.1583 - val_categorical_accuracy: 0.4225
Epoch 305/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1584 - categorical_accuracy: 0.4327 - val_loss: 0.1578 - val_categorical_accuracy: 0.4225
Epoch 306/500
9/9 [==============================] - ETA: 0s - loss: 0.1587 - categorical_accuracy: 0.44 - 0s 3ms/step - loss: 0.1580 - categorical_accuracy: 0.4327 - val_loss: 0.1574 - val_categorical_accuracy: 0.4225
Epoch 307/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1576 - categorical_accuracy: 0.4327 - val_loss: 0.1569 - val_categorical_accuracy: 0.4225
Epoch 308/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1571 - categorical_accuracy: 0.4327 - val_loss: 0.1565 - val_categorical_accuracy: 0.4225
Epoch 309/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1567 - categorical_accuracy: 0.4327 - val_loss: 0.1561 - val_categorical_accuracy: 0.4225
Epoch 310/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1562 - categorical_accuracy: 0.4327 - val_loss: 0.1556 - val_categorical_accuracy: 0.4225
Epoch 311/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1558 - categorical_accuracy: 0.4327 - val_loss: 0.1552 - val_categorical_accuracy: 0.4225
Epoch 312/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1554 - categorical_accuracy: 0.4327 - val_loss: 0.1548 - val_categorical_accuracy: 0.4225
Epoch 313/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1550 - categorical_accuracy: 0.4327 - val_loss: 0.1543 - val_categorical_accuracy: 0.4225
Epoch 314/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1545 - categorical_accuracy: 0.4327 - val_loss: 0.1539 - val_categorical_accuracy: 0.4225
Epoch 315/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1541 - categorical_accuracy: 0.4327 - val_loss: 0.1535 - val_categorical_accuracy: 0.4225
Epoch 316/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1537 - categorical_accuracy: 0.4327 - val_loss: 0.1530 - val_categorical_accuracy: 0.4225
Epoch 317/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1533 - categorical_accuracy: 0.4327 - val_loss: 0.1526 - val_categorical_accuracy: 0.4225
Epoch 318/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1529 - categorical_accuracy: 0.4327 - val_loss: 0.1522 - val_categorical_accuracy: 0.4225
Epoch 319/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1525 - categorical_accuracy: 0.4327 - val_loss: 0.1518 - val_categorical_accuracy: 0.4225
Epoch 320/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1521 - categorical_accuracy: 0.4327 - val_loss: 0.1514 - val_categorical_accuracy: 0.4225
Epoch 321/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1516 - categorical_accuracy: 0.4327 - val_loss: 0.1510 - val_categorical_accuracy: 0.4225
Epoch 322/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1512 - categorical_accuracy: 0.4327 - val_loss: 0.1505 - val_categorical_accuracy: 0.4225
Epoch 323/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1509 - categorical_accuracy: 0.4327 - val_loss: 0.1501 - val_categorical_accuracy: 0.4225
Epoch 324/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1504 - categorical_accuracy: 0.4327 - val_loss: 0.1497 - val_categorical_accuracy: 0.4225
Epoch 325/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1500 - categorical_accuracy: 0.4327 - val_loss: 0.1493 - val_categorical_accuracy: 0.4225
Epoch 326/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1496 - categorical_accuracy: 0.4327 - val_loss: 0.1489 - val_categorical_accuracy: 0.4225
Epoch 327/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1493 - categorical_accuracy: 0.4327 - val_loss: 0.1485 - val_categorical_accuracy: 0.4225
Epoch 328/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1488 - categorical_accuracy: 0.4327 - val_loss: 0.1481 - val_categorical_accuracy: 0.4225
Epoch 329/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1484 - categorical_accuracy: 0.4327 - val_loss: 0.1477 - val_categorical_accuracy: 0.4225
Epoch 330/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1481 - categorical_accuracy: 0.4327 - val_loss: 0.1473 - val_categorical_accuracy: 0.4225
Epoch 331/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1477 - categorical_accuracy: 0.4327 - val_loss: 0.1469 - val_categorical_accuracy: 0.4225
Epoch 332/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1473 - categorical_accuracy: 0.4327 - val_loss: 0.1465 - val_categorical_accuracy: 0.4225
Epoch 333/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1469 - categorical_accuracy: 0.4327 - val_loss: 0.1462 - val_categorical_accuracy: 0.4225
Epoch 334/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1465 - categorical_accuracy: 0.4327 - val_loss: 0.1458 - val_categorical_accuracy: 0.4225
Epoch 335/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1461 - categorical_accuracy: 0.4327 - val_loss: 0.1454 - val_categorical_accuracy: 0.4225
Epoch 336/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1458 - categorical_accuracy: 0.4327 - val_loss: 0.1450 - val_categorical_accuracy: 0.4225
Epoch 337/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1454 - categorical_accuracy: 0.4327 - val_loss: 0.1446 - val_categorical_accuracy: 0.4225
Epoch 338/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1450 - categorical_accuracy: 0.4327 - val_loss: 0.1442 - val_categorical_accuracy: 0.4225
Epoch 339/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1446 - categorical_accuracy: 0.4327 - val_loss: 0.1439 - val_categorical_accuracy: 0.4225
Epoch 340/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1442 - categorical_accuracy: 0.4327 - val_loss: 0.1435 - val_categorical_accuracy: 0.4225
Epoch 341/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1439 - categorical_accuracy: 0.4327 - val_loss: 0.1431 - val_categorical_accuracy: 0.4225
Epoch 342/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1435 - categorical_accuracy: 0.4327 - val_loss: 0.1427 - val_categorical_accuracy: 0.4225
Epoch 343/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1431 - categorical_accuracy: 0.4327 - val_loss: 0.1424 - val_categorical_accuracy: 0.4225
Epoch 344/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1428 - categorical_accuracy: 0.4327 - val_loss: 0.1420 - val_categorical_accuracy: 0.4225
Epoch 345/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1424 - categorical_accuracy: 0.4327 - val_loss: 0.1416 - val_categorical_accuracy: 0.4225
Epoch 346/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1421 - categorical_accuracy: 0.4327 - val_loss: 0.1413 - val_categorical_accuracy: 0.4225
Epoch 347/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1417 - categorical_accuracy: 0.4327 - val_loss: 0.1409 - val_categorical_accuracy: 0.4225
Epoch 348/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1413 - categorical_accuracy: 0.4327 - val_loss: 0.1405 - val_categorical_accuracy: 0.4225
Epoch 349/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1410 - categorical_accuracy: 0.4327 - val_loss: 0.1402 - val_categorical_accuracy: 0.4225
Epoch 350/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1406 - categorical_accuracy: 0.4327 - val_loss: 0.1398 - val_categorical_accuracy: 0.4225
Epoch 351/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1403 - categorical_accuracy: 0.4327 - val_loss: 0.1395 - val_categorical_accuracy: 0.4225
Epoch 352/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1400 - categorical_accuracy: 0.4327 - val_loss: 0.1391 - val_categorical_accuracy: 0.4225
Epoch 353/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1396 - categorical_accuracy: 0.4327 - val_loss: 0.1388 - val_categorical_accuracy: 0.4225
Epoch 354/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1392 - categorical_accuracy: 0.4327 - val_loss: 0.1384 - val_categorical_accuracy: 0.4225
Epoch 355/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1389 - categorical_accuracy: 0.4327 - val_loss: 0.1381 - val_categorical_accuracy: 0.4225
Epoch 356/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1386 - categorical_accuracy: 0.4327 - val_loss: 0.1377 - val_categorical_accuracy: 0.4225
Epoch 357/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1382 - categorical_accuracy: 0.4327 - val_loss: 0.1374 - val_categorical_accuracy: 0.4225
Epoch 358/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1379 - categorical_accuracy: 0.4327 - val_loss: 0.1370 - val_categorical_accuracy: 0.4225
Epoch 359/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1376 - categorical_accuracy: 0.4327 - val_loss: 0.1367 - val_categorical_accuracy: 0.4225
Epoch 360/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1372 - categorical_accuracy: 0.4327 - val_loss: 0.1363 - val_categorical_accuracy: 0.4225
Epoch 361/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1369 - categorical_accuracy: 0.4327 - val_loss: 0.1360 - val_categorical_accuracy: 0.4225
Epoch 362/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1365 - categorical_accuracy: 0.4327 - val_loss: 0.1357 - val_categorical_accuracy: 0.4225
Epoch 363/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1362 - categorical_accuracy: 0.4327 - val_loss: 0.1353 - val_categorical_accuracy: 0.4225
Epoch 364/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1359 - categorical_accuracy: 0.4327 - val_loss: 0.1350 - val_categorical_accuracy: 0.4225
Epoch 365/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1355 - categorical_accuracy: 0.4327 - val_loss: 0.1347 - val_categorical_accuracy: 0.4225
Epoch 366/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1352 - categorical_accuracy: 0.4327 - val_loss: 0.1343 - val_categorical_accuracy: 0.4225
Epoch 367/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1349 - categorical_accuracy: 0.4327 - val_loss: 0.1340 - val_categorical_accuracy: 0.4225
Epoch 368/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1346 - categorical_accuracy: 0.4327 - val_loss: 0.1337 - val_categorical_accuracy: 0.4225
Epoch 369/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1343 - categorical_accuracy: 0.4327 - val_loss: 0.1334 - val_categorical_accuracy: 0.4225
Epoch 370/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1339 - categorical_accuracy: 0.4327 - val_loss: 0.1330 - val_categorical_accuracy: 0.4225
Epoch 371/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1336 - categorical_accuracy: 0.4327 - val_loss: 0.1327 - val_categorical_accuracy: 0.4225
Epoch 372/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1333 - categorical_accuracy: 0.4327 - val_loss: 0.1324 - val_categorical_accuracy: 0.4225
Epoch 373/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1330 - categorical_accuracy: 0.4327 - val_loss: 0.1321 - val_categorical_accuracy: 0.4225
Epoch 374/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1327 - categorical_accuracy: 0.4327 - val_loss: 0.1318 - val_categorical_accuracy: 0.4225
Epoch 375/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1324 - categorical_accuracy: 0.4327 - val_loss: 0.1314 - val_categorical_accuracy: 0.4225
Epoch 376/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1321 - categorical_accuracy: 0.4327 - val_loss: 0.1311 - val_categorical_accuracy: 0.4225
Epoch 377/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1318 - categorical_accuracy: 0.4327 - val_loss: 0.1308 - val_categorical_accuracy: 0.4225
Epoch 378/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1315 - categorical_accuracy: 0.4327 - val_loss: 0.1305 - val_categorical_accuracy: 0.4225
Epoch 379/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1312 - categorical_accuracy: 0.4327 - val_loss: 0.1302 - val_categorical_accuracy: 0.4225
Epoch 380/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1308 - categorical_accuracy: 0.4327 - val_loss: 0.1299 - val_categorical_accuracy: 0.4225
Epoch 381/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1305 - categorical_accuracy: 0.4327 - val_loss: 0.1296 - val_categorical_accuracy: 0.4225
Epoch 382/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1302 - categorical_accuracy: 0.4327 - val_loss: 0.1293 - val_categorical_accuracy: 0.4225
Epoch 383/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1300 - categorical_accuracy: 0.4327 - val_loss: 0.1290 - val_categorical_accuracy: 0.4225
Epoch 384/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1297 - categorical_accuracy: 0.4327 - val_loss: 0.1287 - val_categorical_accuracy: 0.4225
Epoch 385/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1294 - categorical_accuracy: 0.4327 - val_loss: 0.1284 - val_categorical_accuracy: 0.4225
Epoch 386/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1291 - categorical_accuracy: 0.4327 - val_loss: 0.1281 - val_categorical_accuracy: 0.4225
Epoch 387/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1288 - categorical_accuracy: 0.4327 - val_loss: 0.1278 - val_categorical_accuracy: 0.4225
Epoch 388/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1285 - categorical_accuracy: 0.4327 - val_loss: 0.1275 - val_categorical_accuracy: 0.4225
Epoch 389/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1282 - categorical_accuracy: 0.4327 - val_loss: 0.1272 - val_categorical_accuracy: 0.4225
Epoch 390/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1279 - categorical_accuracy: 0.4327 - val_loss: 0.1269 - val_categorical_accuracy: 0.4225
Epoch 391/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1276 - categorical_accuracy: 0.4327 - val_loss: 0.1266 - val_categorical_accuracy: 0.4225
Epoch 392/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1273 - categorical_accuracy: 0.4327 - val_loss: 0.1263 - val_categorical_accuracy: 0.4225
Epoch 393/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1271 - categorical_accuracy: 0.4327 - val_loss: 0.1260 - val_categorical_accuracy: 0.4225
Epoch 394/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1268 - categorical_accuracy: 0.4327 - val_loss: 0.1257 - val_categorical_accuracy: 0.4225
Epoch 395/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1265 - categorical_accuracy: 0.4327 - val_loss: 0.1255 - val_categorical_accuracy: 0.4225
Epoch 396/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1262 - categorical_accuracy: 0.4327 - val_loss: 0.1252 - val_categorical_accuracy: 0.4225
Epoch 397/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1260 - categorical_accuracy: 0.4327 - val_loss: 0.1249 - val_categorical_accuracy: 0.4225
Epoch 398/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1257 - categorical_accuracy: 0.4327 - val_loss: 0.1246 - val_categorical_accuracy: 0.4225
Epoch 399/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1254 - categorical_accuracy: 0.4327 - val_loss: 0.1243 - val_categorical_accuracy: 0.4225
Epoch 400/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1251 - categorical_accuracy: 0.4327 - val_loss: 0.1241 - val_categorical_accuracy: 0.4225
Epoch 401/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1249 - categorical_accuracy: 0.4327 - val_loss: 0.1238 - val_categorical_accuracy: 0.4225
Epoch 402/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1246 - categorical_accuracy: 0.4327 - val_loss: 0.1235 - val_categorical_accuracy: 0.4225
Epoch 403/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1243 - categorical_accuracy: 0.4327 - val_loss: 0.1232 - val_categorical_accuracy: 0.4225
Epoch 404/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1240 - categorical_accuracy: 0.4327 - val_loss: 0.1230 - val_categorical_accuracy: 0.4225
Epoch 405/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1238 - categorical_accuracy: 0.4327 - val_loss: 0.1227 - val_categorical_accuracy: 0.4225
Epoch 406/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1235 - categorical_accuracy: 0.4327 - val_loss: 0.1224 - val_categorical_accuracy: 0.4225
Epoch 407/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1232 - categorical_accuracy: 0.4327 - val_loss: 0.1222 - val_categorical_accuracy: 0.4225
Epoch 408/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1230 - categorical_accuracy: 0.4327 - val_loss: 0.1219 - val_categorical_accuracy: 0.4225
Epoch 409/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1227 - categorical_accuracy: 0.4327 - val_loss: 0.1216 - val_categorical_accuracy: 0.4225
Epoch 410/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1225 - categorical_accuracy: 0.4327 - val_loss: 0.1214 - val_categorical_accuracy: 0.4225
Epoch 411/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1222 - categorical_accuracy: 0.4327 - val_loss: 0.1211 - val_categorical_accuracy: 0.4225
Epoch 412/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1219 - categorical_accuracy: 0.4327 - val_loss: 0.1208 - val_categorical_accuracy: 0.4225
Epoch 413/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1217 - categorical_accuracy: 0.4327 - val_loss: 0.1206 - val_categorical_accuracy: 0.4225
Epoch 414/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1214 - categorical_accuracy: 0.4327 - val_loss: 0.1203 - val_categorical_accuracy: 0.4225
Epoch 415/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1212 - categorical_accuracy: 0.4327 - val_loss: 0.1201 - val_categorical_accuracy: 0.4225
Epoch 416/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1210 - categorical_accuracy: 0.4327 - val_loss: 0.1198 - val_categorical_accuracy: 0.4225
Epoch 417/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1207 - categorical_accuracy: 0.4327 - val_loss: 0.1196 - val_categorical_accuracy: 0.4225
Epoch 418/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1205 - categorical_accuracy: 0.4327 - val_loss: 0.1193 - val_categorical_accuracy: 0.4225
Epoch 419/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1202 - categorical_accuracy: 0.4327 - val_loss: 0.1191 - val_categorical_accuracy: 0.4225
Epoch 420/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1200 - categorical_accuracy: 0.4327 - val_loss: 0.1188 - val_categorical_accuracy: 0.4225
Epoch 421/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1197 - categorical_accuracy: 0.4327 - val_loss: 0.1186 - val_categorical_accuracy: 0.4225
Epoch 422/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1195 - categorical_accuracy: 0.4327 - val_loss: 0.1183 - val_categorical_accuracy: 0.4225
Epoch 423/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1192 - categorical_accuracy: 0.4327 - val_loss: 0.1181 - val_categorical_accuracy: 0.4225
Epoch 424/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1190 - categorical_accuracy: 0.4327 - val_loss: 0.1178 - val_categorical_accuracy: 0.4225
Epoch 425/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1187 - categorical_accuracy: 0.4327 - val_loss: 0.1176 - val_categorical_accuracy: 0.4225
Epoch 426/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1185 - categorical_accuracy: 0.4327 - val_loss: 0.1173 - val_categorical_accuracy: 0.4225
Epoch 427/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1183 - categorical_accuracy: 0.4327 - val_loss: 0.1171 - val_categorical_accuracy: 0.4225
Epoch 428/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1180 - categorical_accuracy: 0.4327 - val_loss: 0.1169 - val_categorical_accuracy: 0.4225
Epoch 429/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1178 - categorical_accuracy: 0.4327 - val_loss: 0.1166 - val_categorical_accuracy: 0.4225
Epoch 430/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1176 - categorical_accuracy: 0.4327 - val_loss: 0.1164 - val_categorical_accuracy: 0.4225
Epoch 431/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1173 - categorical_accuracy: 0.4327 - val_loss: 0.1162 - val_categorical_accuracy: 0.4225
Epoch 432/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1171 - categorical_accuracy: 0.4327 - val_loss: 0.1159 - val_categorical_accuracy: 0.4225
Epoch 433/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1169 - categorical_accuracy: 0.4327 - val_loss: 0.1157 - val_categorical_accuracy: 0.4225
Epoch 434/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1167 - categorical_accuracy: 0.4327 - val_loss: 0.1155 - val_categorical_accuracy: 0.4225
Epoch 435/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1164 - categorical_accuracy: 0.4327 - val_loss: 0.1152 - val_categorical_accuracy: 0.4225
Epoch 436/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1162 - categorical_accuracy: 0.4327 - val_loss: 0.1150 - val_categorical_accuracy: 0.4225
Epoch 437/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1160 - categorical_accuracy: 0.4327 - val_loss: 0.1148 - val_categorical_accuracy: 0.4225
Epoch 438/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1158 - categorical_accuracy: 0.4327 - val_loss: 0.1145 - val_categorical_accuracy: 0.4225
Epoch 439/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1155 - categorical_accuracy: 0.4327 - val_loss: 0.1143 - val_categorical_accuracy: 0.4225
Epoch 440/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1153 - categorical_accuracy: 0.4327 - val_loss: 0.1141 - val_categorical_accuracy: 0.4225
Epoch 441/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1151 - categorical_accuracy: 0.4327 - val_loss: 0.1139 - val_categorical_accuracy: 0.4225
Epoch 442/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1149 - categorical_accuracy: 0.4327 - val_loss: 0.1136 - val_categorical_accuracy: 0.4225
Epoch 443/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1147 - categorical_accuracy: 0.4327 - val_loss: 0.1134 - val_categorical_accuracy: 0.4225
Epoch 444/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1144 - categorical_accuracy: 0.4327 - val_loss: 0.1132 - val_categorical_accuracy: 0.4225
Epoch 445/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1143 - categorical_accuracy: 0.4327 - val_loss: 0.1130 - val_categorical_accuracy: 0.4225
Epoch 446/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1140 - categorical_accuracy: 0.4327 - val_loss: 0.1128 - val_categorical_accuracy: 0.4225
Epoch 447/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1138 - categorical_accuracy: 0.4327 - val_loss: 0.1126 - val_categorical_accuracy: 0.4225
Epoch 448/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1136 - categorical_accuracy: 0.4327 - val_loss: 0.1123 - val_categorical_accuracy: 0.4225
Epoch 449/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1134 - categorical_accuracy: 0.4327 - val_loss: 0.1121 - val_categorical_accuracy: 0.4225
Epoch 450/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1132 - categorical_accuracy: 0.4327 - val_loss: 0.1119 - val_categorical_accuracy: 0.4225
Epoch 451/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1130 - categorical_accuracy: 0.4327 - val_loss: 0.1117 - val_categorical_accuracy: 0.4225
Epoch 452/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1128 - categorical_accuracy: 0.4327 - val_loss: 0.1115 - val_categorical_accuracy: 0.4225
Epoch 453/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1126 - categorical_accuracy: 0.4327 - val_loss: 0.1113 - val_categorical_accuracy: 0.4225
Epoch 454/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1123 - categorical_accuracy: 0.4327 - val_loss: 0.1111 - val_categorical_accuracy: 0.4225
Epoch 455/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1122 - categorical_accuracy: 0.4327 - val_loss: 0.1109 - val_categorical_accuracy: 0.4225
Epoch 456/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1120 - categorical_accuracy: 0.4327 - val_loss: 0.1107 - val_categorical_accuracy: 0.4225
Epoch 457/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1118 - categorical_accuracy: 0.4327 - val_loss: 0.1105 - val_categorical_accuracy: 0.4225
Epoch 458/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1116 - categorical_accuracy: 0.4327 - val_loss: 0.1103 - val_categorical_accuracy: 0.4225
Epoch 459/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1114 - categorical_accuracy: 0.4327 - val_loss: 0.1101 - val_categorical_accuracy: 0.4225
Epoch 460/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1112 - categorical_accuracy: 0.4327 - val_loss: 0.1099 - val_categorical_accuracy: 0.4225
Epoch 461/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1110 - categorical_accuracy: 0.4327 - val_loss: 0.1097 - val_categorical_accuracy: 0.4225
Epoch 462/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - categorical_accuracy: 0.4327 - val_loss: 0.1095 - val_categorical_accuracy: 0.4225
Epoch 463/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1106 - categorical_accuracy: 0.4327 - val_loss: 0.1093 - val_categorical_accuracy: 0.4225
Epoch 464/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1104 - categorical_accuracy: 0.4327 - val_loss: 0.1091 - val_categorical_accuracy: 0.4225
Epoch 465/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1102 - categorical_accuracy: 0.4327 - val_loss: 0.1089 - val_categorical_accuracy: 0.4225
Epoch 466/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1100 - categorical_accuracy: 0.4327 - val_loss: 0.1087 - val_categorical_accuracy: 0.4225
Epoch 467/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1098 - categorical_accuracy: 0.4327 - val_loss: 0.1085 - val_categorical_accuracy: 0.4225
Epoch 468/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1096 - categorical_accuracy: 0.4327 - val_loss: 0.1083 - val_categorical_accuracy: 0.4225
Epoch 469/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1094 - categorical_accuracy: 0.4327 - val_loss: 0.1081 - val_categorical_accuracy: 0.4225
Epoch 470/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1093 - categorical_accuracy: 0.4327 - val_loss: 0.1079 - val_categorical_accuracy: 0.4225
Epoch 471/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1091 - categorical_accuracy: 0.4327 - val_loss: 0.1077 - val_categorical_accuracy: 0.4225
Epoch 472/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1089 - categorical_accuracy: 0.4327 - val_loss: 0.1075 - val_categorical_accuracy: 0.4225
Epoch 473/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1087 - categorical_accuracy: 0.4327 - val_loss: 0.1073 - val_categorical_accuracy: 0.4225
Epoch 474/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1085 - categorical_accuracy: 0.4327 - val_loss: 0.1071 - val_categorical_accuracy: 0.4225
Epoch 475/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1083 - categorical_accuracy: 0.4327 - val_loss: 0.1069 - val_categorical_accuracy: 0.4225
Epoch 476/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1081 - categorical_accuracy: 0.4327 - val_loss: 0.1068 - val_categorical_accuracy: 0.4225
Epoch 477/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1080 - categorical_accuracy: 0.4327 - val_loss: 0.1066 - val_categorical_accuracy: 0.4225
Epoch 478/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1078 - categorical_accuracy: 0.4327 - val_loss: 0.1064 - val_categorical_accuracy: 0.4225
Epoch 479/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1076 - categorical_accuracy: 0.4327 - val_loss: 0.1062 - val_categorical_accuracy: 0.4225
Epoch 480/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1074 - categorical_accuracy: 0.4327 - val_loss: 0.1060 - val_categorical_accuracy: 0.4225
Epoch 481/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1073 - categorical_accuracy: 0.4327 - val_loss: 0.1059 - val_categorical_accuracy: 0.4225
Epoch 482/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1071 - categorical_accuracy: 0.4327 - val_loss: 0.1057 - val_categorical_accuracy: 0.4225
Epoch 483/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1069 - categorical_accuracy: 0.4327 - val_loss: 0.1055 - val_categorical_accuracy: 0.4225
Epoch 484/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1067 - categorical_accuracy: 0.4327 - val_loss: 0.1053 - val_categorical_accuracy: 0.4225
Epoch 485/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1065 - categorical_accuracy: 0.4327 - val_loss: 0.1051 - val_categorical_accuracy: 0.4225
Epoch 486/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1064 - categorical_accuracy: 0.4327 - val_loss: 0.1050 - val_categorical_accuracy: 0.4225
Epoch 487/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1062 - categorical_accuracy: 0.4327 - val_loss: 0.1048 - val_categorical_accuracy: 0.4225
Epoch 488/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1060 - categorical_accuracy: 0.4327 - val_loss: 0.1046 - val_categorical_accuracy: 0.4225
Epoch 489/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1059 - categorical_accuracy: 0.4327 - val_loss: 0.1045 - val_categorical_accuracy: 0.4225
Epoch 490/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1057 - categorical_accuracy: 0.4327 - val_loss: 0.1043 - val_categorical_accuracy: 0.4225
Epoch 491/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1055 - categorical_accuracy: 0.4327 - val_loss: 0.1041 - val_categorical_accuracy: 0.4225
Epoch 492/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1053 - categorical_accuracy: 0.4327 - val_loss: 0.1039 - val_categorical_accuracy: 0.4225
Epoch 493/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1052 - categorical_accuracy: 0.4327 - val_loss: 0.1038 - val_categorical_accuracy: 0.4225
Epoch 494/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1050 - categorical_accuracy: 0.4327 - val_loss: 0.1036 - val_categorical_accuracy: 0.4225
Epoch 495/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1049 - categorical_accuracy: 0.4327 - val_loss: 0.1034 - val_categorical_accuracy: 0.4225
Epoch 496/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1047 - categorical_accuracy: 0.4327 - val_loss: 0.1033 - val_categorical_accuracy: 0.4225
Epoch 497/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1045 - categorical_accuracy: 0.4327 - val_loss: 0.1031 - val_categorical_accuracy: 0.4225
Epoch 498/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1044 - categorical_accuracy: 0.4327 - val_loss: 0.1029 - val_categorical_accuracy: 0.4225
Epoch 499/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1042 - categorical_accuracy: 0.4327 - val_loss: 0.1028 - val_categorical_accuracy: 0.4225
Epoch 500/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1041 - categorical_accuracy: 0.4327 - val_loss: 0.1026 - val_categorical_accuracy: 0.4225
Total Time Taken is : -16.69996452331543
y_pred_cat_2=model_cat_2.predict(X_test)
print("The Accuracy of the model is : ",accuracy_score(y_test,convert_to_class_labels(y_pred_cat_2)))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_test,convert_to_class_labels(y_pred_cat_2)),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.41388888888888886
history=history_cat_2.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["categorical_accuracy"])
ax.set_title("Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
#
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["val_loss"])
ax.set_title("Validation loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["val_categorical_accuracy"])
ax.set_title("Validation Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'categorical_accuracy', 'val_loss', 'val_categorical_accuracy'])
###################################################################
#Regressional Neural Network
###################################################################
model_reg_2=k.Sequential()
model_reg_2.add(BatchNormalization(input_shape=(X_train1.shape[1],)))
model_reg_2.add(Flatten())
model_reg_2.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dropout(0.2, input_shape=(50,)))
model_reg_2.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dropout(0.2, input_shape=(50,)))
model_reg_2.add(Dense(30,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dropout(0.5, input_shape=(50,)))
model_reg_2.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dropout(0.2, input_shape=(50,)))
model_reg_2.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dropout(0.5, input_shape=(30,)))
model_reg_2.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_2.add(Dense(1))
sgd = optimizers.SGD(lr = 0.01,momentum=0.6)
model_reg_2.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.MeanSquaredError())
###################################################################
#
###################################################################
t=time.time()
history_reg_2=model_reg_2.fit(X_train,y_train,validation_data = (X_valid.to_numpy(),y_valid),batch_size=100, epochs = 500) #add verbose later
print("Total Time Taken is : ",t-time.time())
Epoch 1/500 9/9 [==============================] - 0s 24ms/step - loss: 19.9211 - mean_squared_error: 19.8235 - val_loss: 2.2126 - val_mean_squared_error: 2.0943 Epoch 2/500 9/9 [==============================] - 0s 3ms/step - loss: 1.0250 - mean_squared_error: 0.8971 - val_loss: 0.7508 - val_mean_squared_error: 0.6219 Epoch 3/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7801 - mean_squared_error: 0.6525 - val_loss: 0.7382 - val_mean_squared_error: 0.6112 Epoch 4/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7770 - mean_squared_error: 0.6503 - val_loss: 0.7375 - val_mean_squared_error: 0.6117 Epoch 5/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7744 - mean_squared_error: 0.6491 - val_loss: 0.7380 - val_mean_squared_error: 0.6127 Epoch 6/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7768 - mean_squared_error: 0.6519 - val_loss: 0.7352 - val_mean_squared_error: 0.6112 Epoch 7/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7741 - mean_squared_error: 0.6502 - val_loss: 0.7344 - val_mean_squared_error: 0.6113 Epoch 8/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7743 - mean_squared_error: 0.6516 - val_loss: 0.7338 - val_mean_squared_error: 0.6116 Epoch 9/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7798 - mean_squared_error: 0.6580 - val_loss: 0.7325 - val_mean_squared_error: 0.6113 Epoch 10/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7699 - mean_squared_error: 0.6490 - val_loss: 0.7314 - val_mean_squared_error: 0.6112 Epoch 11/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7739 - mean_squared_error: 0.6539 - val_loss: 0.7308 - val_mean_squared_error: 0.6117 Epoch 12/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7749 - mean_squared_error: 0.6560 - val_loss: 0.7312 - val_mean_squared_error: 0.6132 Epoch 13/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7696 - mean_squared_error: 0.6518 - val_loss: 0.7302 - val_mean_squared_error: 0.6124 Epoch 14/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7707 - mean_squared_error: 0.6536 - val_loss: 0.7305 - val_mean_squared_error: 0.6135 Epoch 15/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7688 - mean_squared_error: 0.6522 - val_loss: 0.7270 - val_mean_squared_error: 0.6114 Epoch 16/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7688 - mean_squared_error: 0.6534 - val_loss: 0.7265 - val_mean_squared_error: 0.6116 Epoch 17/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7667 - mean_squared_error: 0.6521 - val_loss: 0.7255 - val_mean_squared_error: 0.6118 Epoch 18/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7644 - mean_squared_error: 0.6511 - val_loss: 0.7253 - val_mean_squared_error: 0.6120 Epoch 19/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7656 - mean_squared_error: 0.6527 - val_loss: 0.7243 - val_mean_squared_error: 0.6119 Epoch 20/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7626 - mean_squared_error: 0.6505 - val_loss: 0.7230 - val_mean_squared_error: 0.6119 Epoch 21/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7627 - mean_squared_error: 0.6517 - val_loss: 0.7226 - val_mean_squared_error: 0.6125 Epoch 22/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7602 - mean_squared_error: 0.6503 - val_loss: 0.7250 - val_mean_squared_error: 0.6149 Epoch 23/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7639 - mean_squared_error: 0.6543 - val_loss: 0.7200 - val_mean_squared_error: 0.6112 Epoch 24/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7617 - mean_squared_error: 0.6530 - val_loss: 0.7198 - val_mean_squared_error: 0.6122 Epoch 25/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7616 - mean_squared_error: 0.6541 - val_loss: 0.7188 - val_mean_squared_error: 0.6115 Epoch 26/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7581 - mean_squared_error: 0.6513 - val_loss: 0.7189 - val_mean_squared_error: 0.6123 Epoch 27/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7607 - mean_squared_error: 0.6544 - val_loss: 0.7168 - val_mean_squared_error: 0.6113 Epoch 28/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7559 - mean_squared_error: 0.6505 - val_loss: 0.7169 - val_mean_squared_error: 0.6125 Epoch 29/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7582 - mean_squared_error: 0.6539 - val_loss: 0.7154 - val_mean_squared_error: 0.6116 Epoch 30/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7544 - mean_squared_error: 0.6509 - val_loss: 0.7156 - val_mean_squared_error: 0.6122 Epoch 31/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7540 - mean_squared_error: 0.6510 - val_loss: 0.7136 - val_mean_squared_error: 0.6114 Epoch 32/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7530 - mean_squared_error: 0.6509 - val_loss: 0.7128 - val_mean_squared_error: 0.6113 Epoch 33/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7529 - mean_squared_error: 0.6516 - val_loss: 0.7125 - val_mean_squared_error: 0.6115 Epoch 34/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7530 - mean_squared_error: 0.6524 - val_loss: 0.7116 - val_mean_squared_error: 0.6114 Epoch 35/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7526 - mean_squared_error: 0.6528 - val_loss: 0.7115 - val_mean_squared_error: 0.6120 Epoch 36/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7502 - mean_squared_error: 0.6510 - val_loss: 0.7097 - val_mean_squared_error: 0.6112 Epoch 37/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7487 - mean_squared_error: 0.6505 - val_loss: 0.7112 - val_mean_squared_error: 0.6130 Epoch 38/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7486 - mean_squared_error: 0.6506 - val_loss: 0.7123 - val_mean_squared_error: 0.6158 Epoch 39/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7498 - mean_squared_error: 0.6534 - val_loss: 0.7085 - val_mean_squared_error: 0.6119 Epoch 40/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7481 - mean_squared_error: 0.6519 - val_loss: 0.7077 - val_mean_squared_error: 0.6118 Epoch 41/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7460 - mean_squared_error: 0.6505 - val_loss: 0.7089 - val_mean_squared_error: 0.6136 Epoch 42/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7480 - mean_squared_error: 0.6532 - val_loss: 0.7068 - val_mean_squared_error: 0.6123 Epoch 43/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7473 - mean_squared_error: 0.6529 - val_loss: 0.7056 - val_mean_squared_error: 0.6124 Epoch 44/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7457 - mean_squared_error: 0.6527 - val_loss: 0.7040 - val_mean_squared_error: 0.6112 Epoch 45/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7433 - mean_squared_error: 0.6509 - val_loss: 0.7036 - val_mean_squared_error: 0.6113 Epoch 46/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7428 - mean_squared_error: 0.6510 - val_loss: 0.7035 - val_mean_squared_error: 0.6119 Epoch 47/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7448 - mean_squared_error: 0.6536 - val_loss: 0.7023 - val_mean_squared_error: 0.6114 Epoch 48/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7411 - mean_squared_error: 0.6505 - val_loss: 0.7013 - val_mean_squared_error: 0.6114 Epoch 49/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7431 - mean_squared_error: 0.6532 - val_loss: 0.7009 - val_mean_squared_error: 0.6114 Epoch 50/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7435 - mean_squared_error: 0.6541 - val_loss: 0.7000 - val_mean_squared_error: 0.6113 Epoch 51/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7397 - mean_squared_error: 0.6512 - val_loss: 0.6994 - val_mean_squared_error: 0.6114 Epoch 52/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7381 - mean_squared_error: 0.6503 - val_loss: 0.6992 - val_mean_squared_error: 0.6116 Epoch 53/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7386 - mean_squared_error: 0.6513 - val_loss: 0.6982 - val_mean_squared_error: 0.6113 Epoch 54/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7366 - mean_squared_error: 0.6501 - val_loss: 0.7001 - val_mean_squared_error: 0.6135 Epoch 55/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7399 - mean_squared_error: 0.6537 - val_loss: 0.6969 - val_mean_squared_error: 0.6113 Epoch 56/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7362 - mean_squared_error: 0.6509 - val_loss: 0.6977 - val_mean_squared_error: 0.6125 Epoch 57/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7368 - mean_squared_error: 0.6520 - val_loss: 0.6980 - val_mean_squared_error: 0.6133 Epoch 58/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7364 - mean_squared_error: 0.6523 - val_loss: 0.6960 - val_mean_squared_error: 0.6120 Epoch 59/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7351 - mean_squared_error: 0.6513 - val_loss: 0.6969 - val_mean_squared_error: 0.6143 Epoch 60/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7351 - mean_squared_error: 0.6526 - val_loss: 0.6993 - val_mean_squared_error: 0.6162 Epoch 61/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7352 - mean_squared_error: 0.6526 - val_loss: 0.6932 - val_mean_squared_error: 0.6112 Epoch 62/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7334 - mean_squared_error: 0.6515 - val_loss: 0.6928 - val_mean_squared_error: 0.6117 Epoch 63/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7342 - mean_squared_error: 0.6531 - val_loss: 0.6919 - val_mean_squared_error: 0.6113 Epoch 64/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7318 - mean_squared_error: 0.6514 - val_loss: 0.6913 - val_mean_squared_error: 0.6112 Epoch 65/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7300 - mean_squared_error: 0.6501 - val_loss: 0.6907 - val_mean_squared_error: 0.6112 Epoch 66/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7301 - mean_squared_error: 0.6508 - val_loss: 0.6904 - val_mean_squared_error: 0.6114 Epoch 67/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7311 - mean_squared_error: 0.6522 - val_loss: 0.6901 - val_mean_squared_error: 0.6116 Epoch 68/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7291 - mean_squared_error: 0.6509 - val_loss: 0.6896 - val_mean_squared_error: 0.6116 Epoch 69/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7289 - mean_squared_error: 0.6512 - val_loss: 0.6889 - val_mean_squared_error: 0.6115 Epoch 70/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7277 - mean_squared_error: 0.6508 - val_loss: 0.6918 - val_mean_squared_error: 0.6146 Epoch 71/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7286 - mean_squared_error: 0.6518 - val_loss: 0.6878 - val_mean_squared_error: 0.6115 Epoch 72/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7265 - mean_squared_error: 0.6507 - val_loss: 0.6883 - val_mean_squared_error: 0.6124 Epoch 73/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7272 - mean_squared_error: 0.6517 - val_loss: 0.6864 - val_mean_squared_error: 0.6113 Epoch 74/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7277 - mean_squared_error: 0.6528 - val_loss: 0.6857 - val_mean_squared_error: 0.6113 Epoch 75/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7276 - mean_squared_error: 0.6533 - val_loss: 0.6858 - val_mean_squared_error: 0.6121 Epoch 76/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7248 - mean_squared_error: 0.6511 - val_loss: 0.6848 - val_mean_squared_error: 0.6113 Epoch 77/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7247 - mean_squared_error: 0.6514 - val_loss: 0.6848 - val_mean_squared_error: 0.6117 Epoch 78/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7234 - mean_squared_error: 0.6506 - val_loss: 0.6839 - val_mean_squared_error: 0.6113 Epoch 79/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7231 - mean_squared_error: 0.6506 - val_loss: 0.6831 - val_mean_squared_error: 0.6112 Epoch 80/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7217 - mean_squared_error: 0.6501 - val_loss: 0.6860 - val_mean_squared_error: 0.6141 Epoch 81/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7236 - mean_squared_error: 0.6521 - val_loss: 0.6834 - val_mean_squared_error: 0.6122 Epoch 82/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7211 - mean_squared_error: 0.6503 - val_loss: 0.6828 - val_mean_squared_error: 0.6121 Epoch 83/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7221 - mean_squared_error: 0.6517 - val_loss: 0.6811 - val_mean_squared_error: 0.6112 Epoch 84/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7219 - mean_squared_error: 0.6522 - val_loss: 0.6807 - val_mean_squared_error: 0.6115 Epoch 85/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7228 - mean_squared_error: 0.6539 - val_loss: 0.6853 - val_mean_squared_error: 0.6158 Epoch 86/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7203 - mean_squared_error: 0.6512 - val_loss: 0.6797 - val_mean_squared_error: 0.6112 Epoch 87/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7201 - mean_squared_error: 0.6518 - val_loss: 0.6802 - val_mean_squared_error: 0.6120 Epoch 88/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7189 - mean_squared_error: 0.6509 - val_loss: 0.6796 - val_mean_squared_error: 0.6124 Epoch 89/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7201 - mean_squared_error: 0.6531 - val_loss: 0.6855 - val_mean_squared_error: 0.6178 Epoch 90/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7191 - mean_squared_error: 0.6520 - val_loss: 0.6786 - val_mean_squared_error: 0.6118 Epoch 91/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7178 - mean_squared_error: 0.6511 - val_loss: 0.6773 - val_mean_squared_error: 0.6112 Epoch 92/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7159 - mean_squared_error: 0.6499 - val_loss: 0.6776 - val_mean_squared_error: 0.6118 Epoch 93/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7178 - mean_squared_error: 0.6522 - val_loss: 0.6773 - val_mean_squared_error: 0.6119 Epoch 94/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7180 - mean_squared_error: 0.6528 - val_loss: 0.6763 - val_mean_squared_error: 0.6115 Epoch 95/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7152 - mean_squared_error: 0.6504 - val_loss: 0.6759 - val_mean_squared_error: 0.6118 Epoch 96/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7162 - mean_squared_error: 0.6521 - val_loss: 0.6756 - val_mean_squared_error: 0.6120 Epoch 97/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7168 - mean_squared_error: 0.6533 - val_loss: 0.6759 - val_mean_squared_error: 0.6122 Epoch 98/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7142 - mean_squared_error: 0.6508 - val_loss: 0.6743 - val_mean_squared_error: 0.6112 Epoch 99/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7138 - mean_squared_error: 0.6509 - val_loss: 0.6740 - val_mean_squared_error: 0.6116 Epoch 100/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7133 - mean_squared_error: 0.6510 - val_loss: 0.6747 - val_mean_squared_error: 0.6123 Epoch 101/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7142 - mean_squared_error: 0.6520 - val_loss: 0.6729 - val_mean_squared_error: 0.6112 Epoch 102/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7127 - mean_squared_error: 0.6514 - val_loss: 0.6779 - val_mean_squared_error: 0.6160 Epoch 103/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7170 - mean_squared_error: 0.6554 - val_loss: 0.6720 - val_mean_squared_error: 0.6112 Epoch 104/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7115 - mean_squared_error: 0.6508 - val_loss: 0.6717 - val_mean_squared_error: 0.6112 Epoch 105/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7101 - mean_squared_error: 0.6498 - val_loss: 0.6746 - val_mean_squared_error: 0.6141 Epoch 106/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7118 - mean_squared_error: 0.6515 - val_loss: 0.6711 - val_mean_squared_error: 0.6116 Epoch 107/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7107 - mean_squared_error: 0.6513 - val_loss: 0.6718 - val_mean_squared_error: 0.6123 Epoch 108/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7101 - mean_squared_error: 0.6510 - val_loss: 0.6717 - val_mean_squared_error: 0.6125 Epoch 109/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7116 - mean_squared_error: 0.6528 - val_loss: 0.6718 - val_mean_squared_error: 0.6130 Epoch 110/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7097 - mean_squared_error: 0.6513 - val_loss: 0.6713 - val_mean_squared_error: 0.6129 Epoch 111/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7081 - mean_squared_error: 0.6500 - val_loss: 0.6690 - val_mean_squared_error: 0.6115 Epoch 112/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7122 - mean_squared_error: 0.6546 - val_loss: 0.6692 - val_mean_squared_error: 0.6123 Epoch 113/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7072 - mean_squared_error: 0.6501 - val_loss: 0.6688 - val_mean_squared_error: 0.6117 Epoch 114/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7089 - mean_squared_error: 0.6520 - val_loss: 0.6679 - val_mean_squared_error: 0.6115 Epoch 115/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7063 - mean_squared_error: 0.6500 - val_loss: 0.6681 - val_mean_squared_error: 0.6118 Epoch 116/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7065 - mean_squared_error: 0.6503 - val_loss: 0.6671 - val_mean_squared_error: 0.6115 Epoch 117/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7070 - mean_squared_error: 0.6516 - val_loss: 0.6707 - val_mean_squared_error: 0.6148 Epoch 118/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7069 - mean_squared_error: 0.6513 - val_loss: 0.6662 - val_mean_squared_error: 0.6113 Epoch 119/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7062 - mean_squared_error: 0.6515 - val_loss: 0.6679 - val_mean_squared_error: 0.6129 Epoch 120/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7071 - mean_squared_error: 0.6525 - val_loss: 0.6687 - val_mean_squared_error: 0.6140 Epoch 121/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7067 - mean_squared_error: 0.6524 - val_loss: 0.6663 - val_mean_squared_error: 0.6122 Epoch 122/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7053 - mean_squared_error: 0.6513 - val_loss: 0.6647 - val_mean_squared_error: 0.6112 Epoch 123/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7030 - mean_squared_error: 0.6495 - val_loss: 0.6646 - val_mean_squared_error: 0.6113 Epoch 124/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7046 - mean_squared_error: 0.6515 - val_loss: 0.6641 - val_mean_squared_error: 0.6113 Epoch 125/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7035 - mean_squared_error: 0.6508 - val_loss: 0.6637 - val_mean_squared_error: 0.6112 Epoch 126/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7034 - mean_squared_error: 0.6511 - val_loss: 0.6637 - val_mean_squared_error: 0.6114 Epoch 127/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7021 - mean_squared_error: 0.6499 - val_loss: 0.6631 - val_mean_squared_error: 0.6112 Epoch 128/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7021 - mean_squared_error: 0.6504 - val_loss: 0.6629 - val_mean_squared_error: 0.6113 Epoch 129/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7018 - mean_squared_error: 0.6503 - val_loss: 0.6624 - val_mean_squared_error: 0.6112 Epoch 130/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7015 - mean_squared_error: 0.6505 - val_loss: 0.6632 - val_mean_squared_error: 0.6122 Epoch 131/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7025 - mean_squared_error: 0.6516 - val_loss: 0.6628 - val_mean_squared_error: 0.6121 Epoch 132/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7027 - mean_squared_error: 0.6522 - val_loss: 0.6648 - val_mean_squared_error: 0.6142 Epoch 133/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7004 - mean_squared_error: 0.6501 - val_loss: 0.6610 - val_mean_squared_error: 0.6112 Epoch 134/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7007 - mean_squared_error: 0.6511 - val_loss: 0.6617 - val_mean_squared_error: 0.6120 Epoch 135/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6988 - mean_squared_error: 0.6492 - val_loss: 0.6610 - val_mean_squared_error: 0.6120 Epoch 136/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7007 - mean_squared_error: 0.6515 - val_loss: 0.6601 - val_mean_squared_error: 0.6112 Epoch 137/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7000 - mean_squared_error: 0.6513 - val_loss: 0.6622 - val_mean_squared_error: 0.6133 Epoch 138/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6996 - mean_squared_error: 0.6509 - val_loss: 0.6596 - val_mean_squared_error: 0.6113 Epoch 139/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6985 - mean_squared_error: 0.6502 - val_loss: 0.6595 - val_mean_squared_error: 0.6114 Epoch 140/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7002 - mean_squared_error: 0.6524 - val_loss: 0.6606 - val_mean_squared_error: 0.6126 Epoch 141/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7004 - mean_squared_error: 0.6526 - val_loss: 0.6586 - val_mean_squared_error: 0.6114 Epoch 142/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6979 - mean_squared_error: 0.6506 - val_loss: 0.6585 - val_mean_squared_error: 0.6114 Epoch 143/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6977 - mean_squared_error: 0.6507 - val_loss: 0.6597 - val_mean_squared_error: 0.6127 Epoch 144/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6979 - mean_squared_error: 0.6510 - val_loss: 0.6576 - val_mean_squared_error: 0.6113 Epoch 145/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6975 - mean_squared_error: 0.6511 - val_loss: 0.6575 - val_mean_squared_error: 0.6115 Epoch 146/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6991 - mean_squared_error: 0.6531 - val_loss: 0.6578 - val_mean_squared_error: 0.6117 Epoch 147/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6968 - mean_squared_error: 0.6510 - val_loss: 0.6569 - val_mean_squared_error: 0.6113 Epoch 148/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6964 - mean_squared_error: 0.6508 - val_loss: 0.6568 - val_mean_squared_error: 0.6114 Epoch 149/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6955 - mean_squared_error: 0.6503 - val_loss: 0.6571 - val_mean_squared_error: 0.6119 Epoch 150/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6956 - mean_squared_error: 0.6506 - val_loss: 0.6567 - val_mean_squared_error: 0.6118 Epoch 151/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6962 - mean_squared_error: 0.6513 - val_loss: 0.6556 - val_mean_squared_error: 0.6112 Epoch 152/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6967 - mean_squared_error: 0.6521 - val_loss: 0.6564 - val_mean_squared_error: 0.6126 Epoch 153/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6958 - mean_squared_error: 0.6519 - val_loss: 0.6551 - val_mean_squared_error: 0.6112 Epoch 154/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6968 - mean_squared_error: 0.6529 - val_loss: 0.6565 - val_mean_squared_error: 0.6133 Epoch 155/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6960 - mean_squared_error: 0.6529 - val_loss: 0.6587 - val_mean_squared_error: 0.6149 Epoch 156/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6957 - mean_squared_error: 0.6520 - val_loss: 0.6543 - val_mean_squared_error: 0.6113 Epoch 157/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6941 - mean_squared_error: 0.6513 - val_loss: 0.6578 - val_mean_squared_error: 0.6146 Epoch 158/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6973 - mean_squared_error: 0.6542 - val_loss: 0.6542 - val_mean_squared_error: 0.6115 Epoch 159/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6940 - mean_squared_error: 0.6514 - val_loss: 0.6548 - val_mean_squared_error: 0.6122 Epoch 160/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6934 - mean_squared_error: 0.6510 - val_loss: 0.6533 - val_mean_squared_error: 0.6112 Epoch 161/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6932 - mean_squared_error: 0.6513 - val_loss: 0.6544 - val_mean_squared_error: 0.6124 Epoch 162/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6931 - mean_squared_error: 0.6513 - val_loss: 0.6550 - val_mean_squared_error: 0.6131 Epoch 163/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6938 - mean_squared_error: 0.6520 - val_loss: 0.6525 - val_mean_squared_error: 0.6112 Epoch 164/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6931 - mean_squared_error: 0.6517 - val_loss: 0.6523 - val_mean_squared_error: 0.6114 Epoch 165/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6919 - mean_squared_error: 0.6508 - val_loss: 0.6520 - val_mean_squared_error: 0.6112 Epoch 166/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6910 - mean_squared_error: 0.6502 - val_loss: 0.6524 - val_mean_squared_error: 0.6122 Epoch 167/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6915 - mean_squared_error: 0.6512 - val_loss: 0.6523 - val_mean_squared_error: 0.6119 Epoch 168/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6907 - mean_squared_error: 0.6505 - val_loss: 0.6525 - val_mean_squared_error: 0.6122 Epoch 169/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6930 - mean_squared_error: 0.6531 - val_loss: 0.6530 - val_mean_squared_error: 0.6129 Epoch 170/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6903 - mean_squared_error: 0.6503 - val_loss: 0.6512 - val_mean_squared_error: 0.6119 Epoch 171/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6905 - mean_squared_error: 0.6511 - val_loss: 0.6516 - val_mean_squared_error: 0.6120 Epoch 172/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6890 - mean_squared_error: 0.6496 - val_loss: 0.6509 - val_mean_squared_error: 0.6120 Epoch 173/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6896 - mean_squared_error: 0.6507 - val_loss: 0.6508 - val_mean_squared_error: 0.6118 Epoch 174/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6912 - mean_squared_error: 0.6520 - val_loss: 0.6540 - val_mean_squared_error: 0.6159 Epoch 175/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6911 - mean_squared_error: 0.6527 - val_loss: 0.6509 - val_mean_squared_error: 0.6123 Epoch 176/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6893 - mean_squared_error: 0.6508 - val_loss: 0.6495 - val_mean_squared_error: 0.6115 Epoch 177/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6890 - mean_squared_error: 0.6509 - val_loss: 0.6497 - val_mean_squared_error: 0.6116 Epoch 178/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6900 - mean_squared_error: 0.6521 - val_loss: 0.6495 - val_mean_squared_error: 0.6116 Epoch 179/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6900 - mean_squared_error: 0.6524 - val_loss: 0.6512 - val_mean_squared_error: 0.6134 Epoch 180/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6892 - mean_squared_error: 0.6516 - val_loss: 0.6493 - val_mean_squared_error: 0.6118 Epoch 181/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6884 - mean_squared_error: 0.6511 - val_loss: 0.6492 - val_mean_squared_error: 0.6119 Epoch 182/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6897 - mean_squared_error: 0.6526 - val_loss: 0.6519 - val_mean_squared_error: 0.6146 Epoch 183/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6882 - mean_squared_error: 0.6512 - val_loss: 0.6479 - val_mean_squared_error: 0.6112 Epoch 184/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6863 - mean_squared_error: 0.6497 - val_loss: 0.6492 - val_mean_squared_error: 0.6125 Epoch 185/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6862 - mean_squared_error: 0.6495 - val_loss: 0.6503 - val_mean_squared_error: 0.6145 Epoch 186/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6897 - mean_squared_error: 0.6538 - val_loss: 0.6504 - val_mean_squared_error: 0.6140 Epoch 187/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6873 - mean_squared_error: 0.6511 - val_loss: 0.6476 - val_mean_squared_error: 0.6120 Epoch 188/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6887 - mean_squared_error: 0.6531 - val_loss: 0.6468 - val_mean_squared_error: 0.6112 Epoch 189/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6862 - mean_squared_error: 0.6505 - val_loss: 0.6477 - val_mean_squared_error: 0.6126 Epoch 190/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6879 - mean_squared_error: 0.6527 - val_loss: 0.6467 - val_mean_squared_error: 0.6114 Epoch 191/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6850 - mean_squared_error: 0.6498 - val_loss: 0.6462 - val_mean_squared_error: 0.6112 Epoch 192/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6889 - mean_squared_error: 0.6538 - val_loss: 0.6486 - val_mean_squared_error: 0.6142 Epoch 193/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6911 - mean_squared_error: 0.6569 - val_loss: 0.6490 - val_mean_squared_error: 0.6140 Epoch 194/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6866 - mean_squared_error: 0.6519 - val_loss: 0.6458 - val_mean_squared_error: 0.6113 Epoch 195/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6846 - mean_squared_error: 0.6502 - val_loss: 0.6466 - val_mean_squared_error: 0.6122 Epoch 196/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6852 - mean_squared_error: 0.6509 - val_loss: 0.6491 - val_mean_squared_error: 0.6146 Epoch 197/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6861 - mean_squared_error: 0.6519 - val_loss: 0.6457 - val_mean_squared_error: 0.6117 Epoch 198/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6858 - mean_squared_error: 0.6521 - val_loss: 0.6480 - val_mean_squared_error: 0.6140 Epoch 199/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6848 - mean_squared_error: 0.6510 - val_loss: 0.6450 - val_mean_squared_error: 0.6114 Epoch 200/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6847 - mean_squared_error: 0.6512 - val_loss: 0.6445 - val_mean_squared_error: 0.6114 Epoch 201/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6869 - mean_squared_error: 0.6536 - val_loss: 0.6454 - val_mean_squared_error: 0.6126 Epoch 202/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6837 - mean_squared_error: 0.6508 - val_loss: 0.6447 - val_mean_squared_error: 0.6116 Epoch 203/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6837 - mean_squared_error: 0.6507 - val_loss: 0.6446 - val_mean_squared_error: 0.6121 Epoch 204/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6880 - mean_squared_error: 0.6555 - val_loss: 0.6462 - val_mean_squared_error: 0.6134 Epoch 205/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6845 - mean_squared_error: 0.6520 - val_loss: 0.6436 - val_mean_squared_error: 0.6112 Epoch 206/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6820 - mean_squared_error: 0.6499 - val_loss: 0.6452 - val_mean_squared_error: 0.6127 Epoch 207/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6839 - mean_squared_error: 0.6516 - val_loss: 0.6432 - val_mean_squared_error: 0.6113 Epoch 208/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6832 - mean_squared_error: 0.6513 - val_loss: 0.6453 - val_mean_squared_error: 0.6132 Epoch 209/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6856 - mean_squared_error: 0.6537 - val_loss: 0.6450 - val_mean_squared_error: 0.6130 Epoch 210/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6825 - mean_squared_error: 0.6507 - val_loss: 0.6433 - val_mean_squared_error: 0.6121 Epoch 211/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6823 - mean_squared_error: 0.6509 - val_loss: 0.6443 - val_mean_squared_error: 0.6127 Epoch 212/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6847 - mean_squared_error: 0.6534 - val_loss: 0.6454 - val_mean_squared_error: 0.6139 Epoch 213/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6848 - mean_squared_error: 0.6536 - val_loss: 0.6424 - val_mean_squared_error: 0.6114 Epoch 214/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6815 - mean_squared_error: 0.6506 - val_loss: 0.6423 - val_mean_squared_error: 0.6117 Epoch 215/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6816 - mean_squared_error: 0.6512 - val_loss: 0.6571 - val_mean_squared_error: 0.6256 Epoch 216/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6908 - mean_squared_error: 0.6599 - val_loss: 0.6426 - val_mean_squared_error: 0.6120 Epoch 217/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6854 - mean_squared_error: 0.6548 - val_loss: 0.6420 - val_mean_squared_error: 0.6115 Epoch 218/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6803 - mean_squared_error: 0.6500 - val_loss: 0.6434 - val_mean_squared_error: 0.6129 Epoch 219/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6831 - mean_squared_error: 0.6530 - val_loss: 0.6511 - val_mean_squared_error: 0.6204 Epoch 220/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6857 - mean_squared_error: 0.6554 - val_loss: 0.6417 - val_mean_squared_error: 0.6117 Epoch 221/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6814 - mean_squared_error: 0.6515 - val_loss: 0.6421 - val_mean_squared_error: 0.6122 Epoch 222/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6800 - mean_squared_error: 0.6503 - val_loss: 0.6416 - val_mean_squared_error: 0.6120 Epoch 223/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6828 - mean_squared_error: 0.6534 - val_loss: 0.6454 - val_mean_squared_error: 0.6156 Epoch 224/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6817 - mean_squared_error: 0.6522 - val_loss: 0.6414 - val_mean_squared_error: 0.6120 Epoch 225/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6802 - mean_squared_error: 0.6508 - val_loss: 0.6403 - val_mean_squared_error: 0.6112 Epoch 226/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6827 - mean_squared_error: 0.6537 - val_loss: 0.6402 - val_mean_squared_error: 0.6112 Epoch 227/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6796 - mean_squared_error: 0.6507 - val_loss: 0.6399 - val_mean_squared_error: 0.6112 Epoch 228/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6787 - mean_squared_error: 0.6500 - val_loss: 0.6420 - val_mean_squared_error: 0.6131 Epoch 229/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6793 - mean_squared_error: 0.6504 - val_loss: 0.6433 - val_mean_squared_error: 0.6153 Epoch 230/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6828 - mean_squared_error: 0.6546 - val_loss: 0.6400 - val_mean_squared_error: 0.6119 Epoch 231/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6820 - mean_squared_error: 0.6540 - val_loss: 0.6404 - val_mean_squared_error: 0.6121 Epoch 232/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6798 - mean_squared_error: 0.6515 - val_loss: 0.6392 - val_mean_squared_error: 0.6112 Epoch 233/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6794 - mean_squared_error: 0.6516 - val_loss: 0.6420 - val_mean_squared_error: 0.6138 Epoch 234/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6788 - mean_squared_error: 0.6508 - val_loss: 0.6404 - val_mean_squared_error: 0.6125 Epoch 235/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6787 - mean_squared_error: 0.6509 - val_loss: 0.6390 - val_mean_squared_error: 0.6114 Epoch 236/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6782 - mean_squared_error: 0.6505 - val_loss: 0.6387 - val_mean_squared_error: 0.6113 Epoch 237/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6789 - mean_squared_error: 0.6513 - val_loss: 0.6401 - val_mean_squared_error: 0.6131 Epoch 238/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6814 - mean_squared_error: 0.6544 - val_loss: 0.6402 - val_mean_squared_error: 0.6128 Epoch 239/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6788 - mean_squared_error: 0.6516 - val_loss: 0.6389 - val_mean_squared_error: 0.6117 Epoch 240/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6789 - mean_squared_error: 0.6516 - val_loss: 0.6386 - val_mean_squared_error: 0.6120 Epoch 241/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6783 - mean_squared_error: 0.6516 - val_loss: 0.6386 - val_mean_squared_error: 0.6117 Epoch 242/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6788 - mean_squared_error: 0.6521 - val_loss: 0.6381 - val_mean_squared_error: 0.6114 Epoch 243/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6768 - mean_squared_error: 0.6502 - val_loss: 0.6409 - val_mean_squared_error: 0.6140 Epoch 244/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6774 - mean_squared_error: 0.6508 - val_loss: 0.6375 - val_mean_squared_error: 0.6113 Epoch 245/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6795 - mean_squared_error: 0.6532 - val_loss: 0.6392 - val_mean_squared_error: 0.6127 Epoch 246/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6769 - mean_squared_error: 0.6506 - val_loss: 0.6373 - val_mean_squared_error: 0.6112 Epoch 247/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6782 - mean_squared_error: 0.6522 - val_loss: 0.6374 - val_mean_squared_error: 0.6114 Epoch 248/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6755 - mean_squared_error: 0.6496 - val_loss: 0.6419 - val_mean_squared_error: 0.6156 Epoch 249/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6788 - mean_squared_error: 0.6528 - val_loss: 0.6370 - val_mean_squared_error: 0.6113 Epoch 250/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6771 - mean_squared_error: 0.6515 - val_loss: 0.6392 - val_mean_squared_error: 0.6133 Epoch 251/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6830 - mean_squared_error: 0.6573 - val_loss: 0.6373 - val_mean_squared_error: 0.6117 Epoch 252/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6768 - mean_squared_error: 0.6513 - val_loss: 0.6391 - val_mean_squared_error: 0.6135 Epoch 253/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6769 - mean_squared_error: 0.6514 - val_loss: 0.6370 - val_mean_squared_error: 0.6120 Epoch 254/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6804 - mean_squared_error: 0.6552 - val_loss: 0.6381 - val_mean_squared_error: 0.6134 Epoch 255/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6764 - mean_squared_error: 0.6514 - val_loss: 0.6383 - val_mean_squared_error: 0.6130 Epoch 256/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6768 - mean_squared_error: 0.6519 - val_loss: 0.6407 - val_mean_squared_error: 0.6154 Epoch 257/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6766 - mean_squared_error: 0.6515 - val_loss: 0.6359 - val_mean_squared_error: 0.6112 Epoch 258/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6750 - mean_squared_error: 0.6503 - val_loss: 0.6374 - val_mean_squared_error: 0.6126 Epoch 259/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6761 - mean_squared_error: 0.6514 - val_loss: 0.6362 - val_mean_squared_error: 0.6116 Epoch 260/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6744 - mean_squared_error: 0.6500 - val_loss: 0.6375 - val_mean_squared_error: 0.6129 Epoch 261/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6786 - mean_squared_error: 0.6541 - val_loss: 0.6354 - val_mean_squared_error: 0.6113 Epoch 262/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6753 - mean_squared_error: 0.6511 - val_loss: 0.6355 - val_mean_squared_error: 0.6113 Epoch 263/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6759 - mean_squared_error: 0.6518 - val_loss: 0.6355 - val_mean_squared_error: 0.6114 Epoch 264/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6749 - mean_squared_error: 0.6509 - val_loss: 0.6373 - val_mean_squared_error: 0.6131 Epoch 265/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6779 - mean_squared_error: 0.6538 - val_loss: 0.6354 - val_mean_squared_error: 0.6118 Epoch 266/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6753 - mean_squared_error: 0.6513 - val_loss: 0.6363 - val_mean_squared_error: 0.6129 Epoch 267/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6767 - mean_squared_error: 0.6533 - val_loss: 0.6349 - val_mean_squared_error: 0.6112 Epoch 268/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6768 - mean_squared_error: 0.6530 - val_loss: 0.6355 - val_mean_squared_error: 0.6122 Epoch 269/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6754 - mean_squared_error: 0.6521 - val_loss: 0.6347 - val_mean_squared_error: 0.6113 Epoch 270/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6749 - mean_squared_error: 0.6517 - val_loss: 0.6417 - val_mean_squared_error: 0.6179 Epoch 271/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6790 - mean_squared_error: 0.6554 - val_loss: 0.6344 - val_mean_squared_error: 0.6112 Epoch 272/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6736 - mean_squared_error: 0.6504 - val_loss: 0.6342 - val_mean_squared_error: 0.6113 Epoch 273/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6743 - mean_squared_error: 0.6513 - val_loss: 0.6345 - val_mean_squared_error: 0.6115 Epoch 274/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6743 - mean_squared_error: 0.6513 - val_loss: 0.6340 - val_mean_squared_error: 0.6112 Epoch 275/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6741 - mean_squared_error: 0.6514 - val_loss: 0.6379 - val_mean_squared_error: 0.6148 Epoch 276/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6736 - mean_squared_error: 0.6507 - val_loss: 0.6339 - val_mean_squared_error: 0.6113 Epoch 277/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6733 - mean_squared_error: 0.6506 - val_loss: 0.6344 - val_mean_squared_error: 0.6117 Epoch 278/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6731 - mean_squared_error: 0.6506 - val_loss: 0.6347 - val_mean_squared_error: 0.6121 Epoch 279/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6732 - mean_squared_error: 0.6506 - val_loss: 0.6337 - val_mean_squared_error: 0.6115 Epoch 280/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6741 - mean_squared_error: 0.6519 - val_loss: 0.6367 - val_mean_squared_error: 0.6141 Epoch 281/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6737 - mean_squared_error: 0.6515 - val_loss: 0.6353 - val_mean_squared_error: 0.6129 Epoch 282/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6758 - mean_squared_error: 0.6537 - val_loss: 0.6386 - val_mean_squared_error: 0.6162 Epoch 283/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6735 - mean_squared_error: 0.6513 - val_loss: 0.6336 - val_mean_squared_error: 0.6116 Epoch 284/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6731 - mean_squared_error: 0.6512 - val_loss: 0.6361 - val_mean_squared_error: 0.6139 Epoch 285/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6727 - mean_squared_error: 0.6507 - val_loss: 0.6330 - val_mean_squared_error: 0.6114 Epoch 286/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6725 - mean_squared_error: 0.6509 - val_loss: 0.6359 - val_mean_squared_error: 0.6140 Epoch 287/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6723 - mean_squared_error: 0.6506 - val_loss: 0.6343 - val_mean_squared_error: 0.6125 Epoch 288/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6723 - mean_squared_error: 0.6506 - val_loss: 0.6327 - val_mean_squared_error: 0.6113 Epoch 289/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6729 - mean_squared_error: 0.6515 - val_loss: 0.6337 - val_mean_squared_error: 0.6126 Epoch 290/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6735 - mean_squared_error: 0.6523 - val_loss: 0.6326 - val_mean_squared_error: 0.6115 Epoch 291/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6735 - mean_squared_error: 0.6525 - val_loss: 0.6371 - val_mean_squared_error: 0.6155 Epoch 292/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6750 - mean_squared_error: 0.6538 - val_loss: 0.6345 - val_mean_squared_error: 0.6131 Epoch 293/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6724 - mean_squared_error: 0.6512 - val_loss: 0.6322 - val_mean_squared_error: 0.6113 Epoch 294/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6727 - mean_squared_error: 0.6518 - val_loss: 0.6327 - val_mean_squared_error: 0.6117 Epoch 295/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6709 - mean_squared_error: 0.6500 - val_loss: 0.6322 - val_mean_squared_error: 0.6113 Epoch 296/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6732 - mean_squared_error: 0.6525 - val_loss: 0.6324 - val_mean_squared_error: 0.6116 Epoch 297/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6709 - mean_squared_error: 0.6502 - val_loss: 0.6330 - val_mean_squared_error: 0.6122 Epoch 298/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6730 - mean_squared_error: 0.6523 - val_loss: 0.6318 - val_mean_squared_error: 0.6112 Epoch 299/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6715 - mean_squared_error: 0.6511 - val_loss: 0.6340 - val_mean_squared_error: 0.6133 Epoch 300/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6749 - mean_squared_error: 0.6543 - val_loss: 0.6322 - val_mean_squared_error: 0.6117 Epoch 301/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6719 - mean_squared_error: 0.6515 - val_loss: 0.6319 - val_mean_squared_error: 0.6115 Epoch 302/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6745 - mean_squared_error: 0.6541 - val_loss: 0.6337 - val_mean_squared_error: 0.6139 Epoch 303/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6747 - mean_squared_error: 0.6545 - val_loss: 0.6317 - val_mean_squared_error: 0.6118 Epoch 304/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6709 - mean_squared_error: 0.6511 - val_loss: 0.6343 - val_mean_squared_error: 0.6140 Epoch 305/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6709 - mean_squared_error: 0.6508 - val_loss: 0.6313 - val_mean_squared_error: 0.6113 Epoch 306/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6711 - mean_squared_error: 0.6511 - val_loss: 0.6312 - val_mean_squared_error: 0.6115 Epoch 307/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6739 - mean_squared_error: 0.6541 - val_loss: 0.6309 - val_mean_squared_error: 0.6112 Epoch 308/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6713 - mean_squared_error: 0.6517 - val_loss: 0.6352 - val_mean_squared_error: 0.6151 Epoch 309/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6718 - mean_squared_error: 0.6519 - val_loss: 0.6310 - val_mean_squared_error: 0.6113 Epoch 310/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6719 - mean_squared_error: 0.6521 - val_loss: 0.6322 - val_mean_squared_error: 0.6130 Epoch 311/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6702 - mean_squared_error: 0.6509 - val_loss: 0.6360 - val_mean_squared_error: 0.6161 Epoch 312/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6736 - mean_squared_error: 0.6542 - val_loss: 0.6326 - val_mean_squared_error: 0.6130 Epoch 313/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6696 - mean_squared_error: 0.6499 - val_loss: 0.6382 - val_mean_squared_error: 0.6195 Epoch 314/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6733 - mean_squared_error: 0.6542 - val_loss: 0.6304 - val_mean_squared_error: 0.6112 Epoch 315/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6729 - mean_squared_error: 0.6538 - val_loss: 0.6303 - val_mean_squared_error: 0.6112 Epoch 316/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6716 - mean_squared_error: 0.6526 - val_loss: 0.6302 - val_mean_squared_error: 0.6112 Epoch 317/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6690 - mean_squared_error: 0.6499 - val_loss: 0.6333 - val_mean_squared_error: 0.6140 Epoch 318/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6714 - mean_squared_error: 0.6521 - val_loss: 0.6301 - val_mean_squared_error: 0.6113 Epoch 319/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6691 - mean_squared_error: 0.6502 - val_loss: 0.6304 - val_mean_squared_error: 0.6115 Epoch 320/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6693 - mean_squared_error: 0.6505 - val_loss: 0.6329 - val_mean_squared_error: 0.6138 Epoch 321/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6725 - mean_squared_error: 0.6535 - val_loss: 0.6301 - val_mean_squared_error: 0.6113 Epoch 322/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6701 - mean_squared_error: 0.6513 - val_loss: 0.6303 - val_mean_squared_error: 0.6119 Epoch 323/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6713 - mean_squared_error: 0.6528 - val_loss: 0.6298 - val_mean_squared_error: 0.6112 Epoch 324/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6713 - mean_squared_error: 0.6529 - val_loss: 0.6299 - val_mean_squared_error: 0.6114 Epoch 325/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6707 - mean_squared_error: 0.6521 - val_loss: 0.6300 - val_mean_squared_error: 0.6118 Epoch 326/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6698 - mean_squared_error: 0.6514 - val_loss: 0.6296 - val_mean_squared_error: 0.6114 Epoch 327/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6733 - mean_squared_error: 0.6553 - val_loss: 0.6362 - val_mean_squared_error: 0.6175 Epoch 328/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6690 - mean_squared_error: 0.6506 - val_loss: 0.6294 - val_mean_squared_error: 0.6113 Epoch 329/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6692 - mean_squared_error: 0.6510 - val_loss: 0.6293 - val_mean_squared_error: 0.6113 Epoch 330/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6723 - mean_squared_error: 0.6544 - val_loss: 0.6374 - val_mean_squared_error: 0.6188 Epoch 331/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6691 - mean_squared_error: 0.6509 - val_loss: 0.6292 - val_mean_squared_error: 0.6113 Epoch 332/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6695 - mean_squared_error: 0.6514 - val_loss: 0.6300 - val_mean_squared_error: 0.6124 Epoch 333/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6687 - mean_squared_error: 0.6510 - val_loss: 0.6305 - val_mean_squared_error: 0.6125 Epoch 334/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6686 - mean_squared_error: 0.6508 - val_loss: 0.6289 - val_mean_squared_error: 0.6112 Epoch 335/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6681 - mean_squared_error: 0.6503 - val_loss: 0.6294 - val_mean_squared_error: 0.6116 Epoch 336/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6681 - mean_squared_error: 0.6503 - val_loss: 0.6289 - val_mean_squared_error: 0.6114 Epoch 337/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6713 - mean_squared_error: 0.6537 - val_loss: 0.6302 - val_mean_squared_error: 0.6124 Epoch 338/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6691 - mean_squared_error: 0.6513 - val_loss: 0.6325 - val_mean_squared_error: 0.6155 Epoch 339/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6699 - mean_squared_error: 0.6526 - val_loss: 0.6297 - val_mean_squared_error: 0.6121 Epoch 340/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6687 - mean_squared_error: 0.6513 - val_loss: 0.6300 - val_mean_squared_error: 0.6124 Epoch 341/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6693 - mean_squared_error: 0.6519 - val_loss: 0.6286 - val_mean_squared_error: 0.6113 Epoch 342/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6691 - mean_squared_error: 0.6519 - val_loss: 0.6302 - val_mean_squared_error: 0.6128 Epoch 343/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6686 - mean_squared_error: 0.6512 - val_loss: 0.6283 - val_mean_squared_error: 0.6112 Epoch 344/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6684 - mean_squared_error: 0.6513 - val_loss: 0.6316 - val_mean_squared_error: 0.6142 Epoch 345/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6689 - mean_squared_error: 0.6516 - val_loss: 0.6282 - val_mean_squared_error: 0.6112 Epoch 346/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6681 - mean_squared_error: 0.6510 - val_loss: 0.6285 - val_mean_squared_error: 0.6117 Epoch 347/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6698 - mean_squared_error: 0.6527 - val_loss: 0.6297 - val_mean_squared_error: 0.6131 Epoch 348/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6702 - mean_squared_error: 0.6535 - val_loss: 0.6305 - val_mean_squared_error: 0.6133 Epoch 349/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6697 - mean_squared_error: 0.6528 - val_loss: 0.6283 - val_mean_squared_error: 0.6115 Epoch 350/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6676 - mean_squared_error: 0.6507 - val_loss: 0.6287 - val_mean_squared_error: 0.6122 Epoch 351/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6673 - mean_squared_error: 0.6508 - val_loss: 0.6345 - val_mean_squared_error: 0.6174 Epoch 352/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6710 - mean_squared_error: 0.6541 - val_loss: 0.6283 - val_mean_squared_error: 0.6119 Epoch 353/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6666 - mean_squared_error: 0.6500 - val_loss: 0.6291 - val_mean_squared_error: 0.6124 Epoch 354/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6677 - mean_squared_error: 0.6509 - val_loss: 0.6287 - val_mean_squared_error: 0.6125 Epoch 355/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6679 - mean_squared_error: 0.6515 - val_loss: 0.6280 - val_mean_squared_error: 0.6115 Epoch 356/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6706 - mean_squared_error: 0.6541 - val_loss: 0.6278 - val_mean_squared_error: 0.6116 Epoch 357/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6684 - mean_squared_error: 0.6520 - val_loss: 0.6275 - val_mean_squared_error: 0.6113 Epoch 358/500 9/9 [==============================] - ETA: 0s - loss: 0.7380 - mean_squared_error: 0.72 - 0s 2ms/step - loss: 0.6678 - mean_squared_error: 0.6515 - val_loss: 0.6275 - val_mean_squared_error: 0.6113 Epoch 359/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6699 - mean_squared_error: 0.6538 - val_loss: 0.6317 - val_mean_squared_error: 0.6151 Epoch 360/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6687 - mean_squared_error: 0.6523 - val_loss: 0.6276 - val_mean_squared_error: 0.6116 Epoch 361/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6673 - mean_squared_error: 0.6511 - val_loss: 0.6272 - val_mean_squared_error: 0.6112 Epoch 362/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6666 - mean_squared_error: 0.6506 - val_loss: 0.6295 - val_mean_squared_error: 0.6133 Epoch 363/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6678 - mean_squared_error: 0.6516 - val_loss: 0.6273 - val_mean_squared_error: 0.6114 Epoch 364/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6673 - mean_squared_error: 0.6513 - val_loss: 0.6271 - val_mean_squared_error: 0.6112 Epoch 365/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6672 - mean_squared_error: 0.6513 - val_loss: 0.6271 - val_mean_squared_error: 0.6112 Epoch 366/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6707 - mean_squared_error: 0.6547 - val_loss: 0.6270 - val_mean_squared_error: 0.6112 Epoch 367/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6668 - mean_squared_error: 0.6509 - val_loss: 0.6274 - val_mean_squared_error: 0.6115 Epoch 368/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6670 - mean_squared_error: 0.6512 - val_loss: 0.6276 - val_mean_squared_error: 0.6118 Epoch 369/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6664 - mean_squared_error: 0.6506 - val_loss: 0.6288 - val_mean_squared_error: 0.6135 Epoch 370/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6728 - mean_squared_error: 0.6573 - val_loss: 0.6273 - val_mean_squared_error: 0.6116 Epoch 371/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6668 - mean_squared_error: 0.6513 - val_loss: 0.6267 - val_mean_squared_error: 0.6112 Epoch 372/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6679 - mean_squared_error: 0.6525 - val_loss: 0.6320 - val_mean_squared_error: 0.6161 Epoch 373/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6684 - mean_squared_error: 0.6528 - val_loss: 0.6276 - val_mean_squared_error: 0.6120 Epoch 374/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6662 - mean_squared_error: 0.6507 - val_loss: 0.6266 - val_mean_squared_error: 0.6112 Epoch 375/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6666 - mean_squared_error: 0.6513 - val_loss: 0.6273 - val_mean_squared_error: 0.6118 Epoch 376/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6695 - mean_squared_error: 0.6540 - val_loss: 0.6272 - val_mean_squared_error: 0.6122 Epoch 377/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6655 - mean_squared_error: 0.6504 - val_loss: 0.6302 - val_mean_squared_error: 0.6147 Epoch 378/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6667 - mean_squared_error: 0.6512 - val_loss: 0.6266 - val_mean_squared_error: 0.6116 Epoch 379/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6675 - mean_squared_error: 0.6523 - val_loss: 0.6265 - val_mean_squared_error: 0.6116 Epoch 380/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6664 - mean_squared_error: 0.6514 - val_loss: 0.6283 - val_mean_squared_error: 0.6130 Epoch 381/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6670 - mean_squared_error: 0.6519 - val_loss: 0.6262 - val_mean_squared_error: 0.6113 Epoch 382/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6662 - mean_squared_error: 0.6512 - val_loss: 0.6278 - val_mean_squared_error: 0.6126 Epoch 383/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6660 - mean_squared_error: 0.6510 - val_loss: 0.6287 - val_mean_squared_error: 0.6135 Epoch 384/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6655 - mean_squared_error: 0.6505 - val_loss: 0.6265 - val_mean_squared_error: 0.6115 Epoch 385/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6669 - mean_squared_error: 0.6519 - val_loss: 0.6262 - val_mean_squared_error: 0.6113 Epoch 386/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6654 - mean_squared_error: 0.6506 - val_loss: 0.6280 - val_mean_squared_error: 0.6130 Epoch 387/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6681 - mean_squared_error: 0.6533 - val_loss: 0.6292 - val_mean_squared_error: 0.6142 Epoch 388/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6658 - mean_squared_error: 0.6509 - val_loss: 0.6259 - val_mean_squared_error: 0.6112 Epoch 389/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6650 - mean_squared_error: 0.6504 - val_loss: 0.6276 - val_mean_squared_error: 0.6128 Epoch 390/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6653 - mean_squared_error: 0.6505 - val_loss: 0.6265 - val_mean_squared_error: 0.6121 Epoch 391/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6706 - mean_squared_error: 0.6560 - val_loss: 0.6258 - val_mean_squared_error: 0.6113 Epoch 392/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6649 - mean_squared_error: 0.6504 - val_loss: 0.6258 - val_mean_squared_error: 0.6114 Epoch 393/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6664 - mean_squared_error: 0.6519 - val_loss: 0.6265 - val_mean_squared_error: 0.6123 Epoch 394/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6666 - mean_squared_error: 0.6522 - val_loss: 0.6275 - val_mean_squared_error: 0.6129 Epoch 395/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6662 - mean_squared_error: 0.6515 - val_loss: 0.6259 - val_mean_squared_error: 0.6117 Epoch 396/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6653 - mean_squared_error: 0.6509 - val_loss: 0.6255 - val_mean_squared_error: 0.6112 Epoch 397/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6662 - mean_squared_error: 0.6519 - val_loss: 0.6258 - val_mean_squared_error: 0.6114 Epoch 398/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6650 - mean_squared_error: 0.6507 - val_loss: 0.6254 - val_mean_squared_error: 0.6113 Epoch 399/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6655 - mean_squared_error: 0.6513 - val_loss: 0.6257 - val_mean_squared_error: 0.6114 Epoch 400/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6676 - mean_squared_error: 0.6533 - val_loss: 0.6260 - val_mean_squared_error: 0.6121 Epoch 401/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6682 - mean_squared_error: 0.6539 - val_loss: 0.6277 - val_mean_squared_error: 0.6139 Epoch 402/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6512 - val_loss: 0.6270 - val_mean_squared_error: 0.6128 Epoch 403/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6651 - mean_squared_error: 0.6510 - val_loss: 0.6252 - val_mean_squared_error: 0.6112 Epoch 404/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6653 - mean_squared_error: 0.6513 - val_loss: 0.6253 - val_mean_squared_error: 0.6113 Epoch 405/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6644 - mean_squared_error: 0.6505 - val_loss: 0.6259 - val_mean_squared_error: 0.6119 Epoch 406/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6653 - mean_squared_error: 0.6512 - val_loss: 0.6266 - val_mean_squared_error: 0.6130 Epoch 407/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6649 - mean_squared_error: 0.6510 - val_loss: 0.6252 - val_mean_squared_error: 0.6113 Epoch 408/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6649 - mean_squared_error: 0.6510 - val_loss: 0.6264 - val_mean_squared_error: 0.6128 Epoch 409/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6668 - mean_squared_error: 0.6531 - val_loss: 0.6261 - val_mean_squared_error: 0.6126 Epoch 410/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6661 - mean_squared_error: 0.6525 - val_loss: 0.6254 - val_mean_squared_error: 0.6116 Epoch 411/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6642 - mean_squared_error: 0.6504 - val_loss: 0.6266 - val_mean_squared_error: 0.6127 Epoch 412/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6648 - mean_squared_error: 0.6510 - val_loss: 0.6249 - val_mean_squared_error: 0.6113 Epoch 413/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6655 - mean_squared_error: 0.6520 - val_loss: 0.6299 - val_mean_squared_error: 0.6159 Epoch 414/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6647 - mean_squared_error: 0.6508 - val_loss: 0.6252 - val_mean_squared_error: 0.6119 Epoch 415/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6655 - mean_squared_error: 0.6520 - val_loss: 0.6247 - val_mean_squared_error: 0.6112 Epoch 416/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6669 - mean_squared_error: 0.6537 - val_loss: 0.6335 - val_mean_squared_error: 0.6195 Epoch 417/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6725 - mean_squared_error: 0.6588 - val_loss: 0.6250 - val_mean_squared_error: 0.6115 Epoch 418/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6637 - mean_squared_error: 0.6503 - val_loss: 0.6277 - val_mean_squared_error: 0.6140 Epoch 419/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6642 - mean_squared_error: 0.6505 - val_loss: 0.6280 - val_mean_squared_error: 0.6151 Epoch 420/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6689 - mean_squared_error: 0.6557 - val_loss: 0.6250 - val_mean_squared_error: 0.6118 Epoch 421/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6659 - mean_squared_error: 0.6525 - val_loss: 0.6245 - val_mean_squared_error: 0.6112 Epoch 422/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6521 - val_loss: 0.6319 - val_mean_squared_error: 0.6181 Epoch 423/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6666 - mean_squared_error: 0.6531 - val_loss: 0.6250 - val_mean_squared_error: 0.6120 Epoch 424/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6648 - mean_squared_error: 0.6517 - val_loss: 0.6271 - val_mean_squared_error: 0.6136 Epoch 425/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6660 - mean_squared_error: 0.6527 - val_loss: 0.6291 - val_mean_squared_error: 0.6156 Epoch 426/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6675 - mean_squared_error: 0.6543 - val_loss: 0.6247 - val_mean_squared_error: 0.6115 Epoch 427/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6657 - mean_squared_error: 0.6525 - val_loss: 0.6265 - val_mean_squared_error: 0.6132 Epoch 428/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6639 - mean_squared_error: 0.6509 - val_loss: 0.6270 - val_mean_squared_error: 0.6137 Epoch 429/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6724 - mean_squared_error: 0.6594 - val_loss: 0.6276 - val_mean_squared_error: 0.6144 Epoch 430/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6681 - mean_squared_error: 0.6549 - val_loss: 0.6245 - val_mean_squared_error: 0.6115 Epoch 431/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6634 - mean_squared_error: 0.6503 - val_loss: 0.6243 - val_mean_squared_error: 0.6115 Epoch 432/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6644 - mean_squared_error: 0.6515 - val_loss: 0.6248 - val_mean_squared_error: 0.6118 Epoch 433/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6632 - mean_squared_error: 0.6503 - val_loss: 0.6275 - val_mean_squared_error: 0.6144 Epoch 434/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6653 - mean_squared_error: 0.6521 - val_loss: 0.6244 - val_mean_squared_error: 0.6118 Epoch 435/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6508 - val_loss: 0.6260 - val_mean_squared_error: 0.6130 Epoch 436/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6641 - mean_squared_error: 0.6512 - val_loss: 0.6240 - val_mean_squared_error: 0.6113 Epoch 437/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6639 - mean_squared_error: 0.6510 - val_loss: 0.6246 - val_mean_squared_error: 0.6120 Epoch 438/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6637 - mean_squared_error: 0.6510 - val_loss: 0.6247 - val_mean_squared_error: 0.6119 Epoch 439/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6647 - mean_squared_error: 0.6520 - val_loss: 0.6241 - val_mean_squared_error: 0.6114 Epoch 440/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6508 - val_loss: 0.6257 - val_mean_squared_error: 0.6134 Epoch 441/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6645 - mean_squared_error: 0.6521 - val_loss: 0.6277 - val_mean_squared_error: 0.6149 Epoch 442/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6675 - mean_squared_error: 0.6549 - val_loss: 0.6255 - val_mean_squared_error: 0.6127 Epoch 443/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6628 - mean_squared_error: 0.6502 - val_loss: 0.6247 - val_mean_squared_error: 0.6124 Epoch 444/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6528 - val_loss: 0.6236 - val_mean_squared_error: 0.6113 Epoch 445/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6638 - mean_squared_error: 0.6513 - val_loss: 0.6252 - val_mean_squared_error: 0.6131 Epoch 446/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6666 - mean_squared_error: 0.6544 - val_loss: 0.6256 - val_mean_squared_error: 0.6130 Epoch 447/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6640 - mean_squared_error: 0.6514 - val_loss: 0.6261 - val_mean_squared_error: 0.6140 Epoch 448/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6661 - mean_squared_error: 0.6538 - val_loss: 0.6235 - val_mean_squared_error: 0.6112 Epoch 449/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6663 - mean_squared_error: 0.6540 - val_loss: 0.6235 - val_mean_squared_error: 0.6112 Epoch 450/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_squared_error: 0.6534 - val_loss: 0.6234 - val_mean_squared_error: 0.6112 Epoch 451/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6634 - mean_squared_error: 0.6511 - val_loss: 0.6240 - val_mean_squared_error: 0.6116 Epoch 452/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6648 - mean_squared_error: 0.6525 - val_loss: 0.6239 - val_mean_squared_error: 0.6116 Epoch 453/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6619 - mean_squared_error: 0.6496 - val_loss: 0.6262 - val_mean_squared_error: 0.6143 Epoch 454/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6640 - mean_squared_error: 0.6521 - val_loss: 0.6294 - val_mean_squared_error: 0.6168 Epoch 455/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6667 - mean_squared_error: 0.6544 - val_loss: 0.6236 - val_mean_squared_error: 0.6117 Epoch 456/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6639 - mean_squared_error: 0.6517 - val_loss: 0.6234 - val_mean_squared_error: 0.6114 Epoch 457/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6638 - mean_squared_error: 0.6518 - val_loss: 0.6233 - val_mean_squared_error: 0.6112 Epoch 458/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6632 - mean_squared_error: 0.6512 - val_loss: 0.6250 - val_mean_squared_error: 0.6128 Epoch 459/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6632 - mean_squared_error: 0.6510 - val_loss: 0.6231 - val_mean_squared_error: 0.6112 Epoch 460/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6625 - mean_squared_error: 0.6507 - val_loss: 0.6269 - val_mean_squared_error: 0.6147 Epoch 461/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6644 - mean_squared_error: 0.6524 - val_loss: 0.6275 - val_mean_squared_error: 0.6153 Epoch 462/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_squared_error: 0.6535 - val_loss: 0.6235 - val_mean_squared_error: 0.6115 Epoch 463/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6516 - val_loss: 0.6236 - val_mean_squared_error: 0.6117 Epoch 464/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6622 - mean_squared_error: 0.6504 - val_loss: 0.6240 - val_mean_squared_error: 0.6121 Epoch 465/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6664 - mean_squared_error: 0.6544 - val_loss: 0.6232 - val_mean_squared_error: 0.6116 Epoch 466/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6654 - mean_squared_error: 0.6539 - val_loss: 0.6316 - val_mean_squared_error: 0.6194 Epoch 467/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_squared_error: 0.6538 - val_loss: 0.6246 - val_mean_squared_error: 0.6127 Epoch 468/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6685 - mean_squared_error: 0.6566 - val_loss: 0.6230 - val_mean_squared_error: 0.6113 Epoch 469/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6633 - mean_squared_error: 0.6514 - val_loss: 0.6243 - val_mean_squared_error: 0.6128 Epoch 470/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6633 - mean_squared_error: 0.6518 - val_loss: 0.6241 - val_mean_squared_error: 0.6122 Epoch 471/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6629 - mean_squared_error: 0.6512 - val_loss: 0.6229 - val_mean_squared_error: 0.6113 Epoch 472/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6638 - mean_squared_error: 0.6520 - val_loss: 0.6244 - val_mean_squared_error: 0.6131 Epoch 473/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6671 - mean_squared_error: 0.6557 - val_loss: 0.6233 - val_mean_squared_error: 0.6116 Epoch 474/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6623 - mean_squared_error: 0.6505 - val_loss: 0.6249 - val_mean_squared_error: 0.6137 Epoch 475/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6650 - mean_squared_error: 0.6536 - val_loss: 0.6232 - val_mean_squared_error: 0.6116 Epoch 476/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6627 - mean_squared_error: 0.6511 - val_loss: 0.6232 - val_mean_squared_error: 0.6116 Epoch 477/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6633 - mean_squared_error: 0.6518 - val_loss: 0.6227 - val_mean_squared_error: 0.6113 Epoch 478/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6623 - mean_squared_error: 0.6509 - val_loss: 0.6251 - val_mean_squared_error: 0.6134 Epoch 479/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6665 - mean_squared_error: 0.6551 - val_loss: 0.6273 - val_mean_squared_error: 0.6155 Epoch 480/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6642 - mean_squared_error: 0.6529 - val_loss: 0.6263 - val_mean_squared_error: 0.6146 Epoch 481/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6620 - mean_squared_error: 0.6504 - val_loss: 0.6241 - val_mean_squared_error: 0.6130 Epoch 482/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6638 - mean_squared_error: 0.6526 - val_loss: 0.6229 - val_mean_squared_error: 0.6115 Epoch 483/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_squared_error: 0.6545 - val_loss: 0.6258 - val_mean_squared_error: 0.6142 Epoch 484/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6639 - mean_squared_error: 0.6525 - val_loss: 0.6225 - val_mean_squared_error: 0.6113 Epoch 485/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6619 - mean_squared_error: 0.6506 - val_loss: 0.6283 - val_mean_squared_error: 0.6167 Epoch 486/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6620 - mean_squared_error: 0.6507 - val_loss: 0.6224 - val_mean_squared_error: 0.6112 Epoch 487/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6622 - mean_squared_error: 0.6509 - val_loss: 0.6225 - val_mean_squared_error: 0.6113 Epoch 488/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6621 - mean_squared_error: 0.6509 - val_loss: 0.6226 - val_mean_squared_error: 0.6114 Epoch 489/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6611 - mean_squared_error: 0.6500 - val_loss: 0.6301 - val_mean_squared_error: 0.6185 Epoch 490/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6641 - mean_squared_error: 0.6528 - val_loss: 0.6223 - val_mean_squared_error: 0.6112 Epoch 491/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6616 - mean_squared_error: 0.6505 - val_loss: 0.6223 - val_mean_squared_error: 0.6112 Epoch 492/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6649 - mean_squared_error: 0.6537 - val_loss: 0.6233 - val_mean_squared_error: 0.6124 Epoch 493/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6627 - mean_squared_error: 0.6517 - val_loss: 0.6229 - val_mean_squared_error: 0.6118 Epoch 494/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6629 - mean_squared_error: 0.6519 - val_loss: 0.6265 - val_mean_squared_error: 0.6152 Epoch 495/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6643 - mean_squared_error: 0.6532 - val_loss: 0.6222 - val_mean_squared_error: 0.6112 Epoch 496/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6637 - mean_squared_error: 0.6526 - val_loss: 0.6221 - val_mean_squared_error: 0.6112 Epoch 497/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6614 - mean_squared_error: 0.6503 - val_loss: 0.6221 - val_mean_squared_error: 0.6112 Epoch 498/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6614 - mean_squared_error: 0.6505 - val_loss: 0.6299 - val_mean_squared_error: 0.6185 Epoch 499/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6639 - mean_squared_error: 0.6527 - val_loss: 0.6225 - val_mean_squared_error: 0.6118 Epoch 500/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6620 - mean_squared_error: 0.6512 - val_loss: 0.6259 - val_mean_squared_error: 0.6147 Total Time Taken is : -14.055402517318726
y_pred_reg_2=model_reg_2.predict(X_test).astype("int64")
print("The Accuracy of the model is : ",accuracy_score(y_test,y_pred_reg_2))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_test,y_pred_reg_2),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.41388888888888886
history=history_reg_2.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["mean_squared_error"])
ax.set_title("Mean Squared Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
#
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["val_loss"])
ax.set_title("Validation loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["val_mean_squared_error"])
ax.set_title("Validation Mean Squared Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'mean_squared_error', 'val_loss', 'val_mean_squared_error'])
Looks like some serious overfitting going on with Categorical Neural Network.
Some Principal Component Analysis Let us do some principal component analysis to eliminate some unknown or redundant dimensions in the data, and redo the whole thing again.
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
data = StandardScaler().fit_transform(data)
pca = PCA(n_components=6)
data_in = pca.fit_transform(data)
data = pd.DataFrame(data = data_in)
X_train1, X_valid, y_train1, y_valid = train_test_split(data, data_org["Signal_Strength"], random_state=0)
X_train,X_test,y_train,y_test=train_test_split(X_train1,y_train1,test_size=0.30,random_state=0)
###################################################################
#Categorical Neural Network
###################################################################
model_cat_3=k.Sequential()
#model_cat_3.add(Flatten(input_shape=(X_train.shape[1],)))
#model_cat_3.add(Reshape((784,),input_shape=(X_train.shape[0],X_train.shape[1],)))
model_cat_3.add(BatchNormalization())
model_cat_3.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.2, input_shape=(60,)))
model_cat_3.add(Dense(30,activation="relu",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.2, input_shape=(30,)))
model_cat_3.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.4, input_shape=(60,)))
model_cat_3.add(Dense(60,activation="relu",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.2, input_shape=(60,)))
model_cat_3.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.4, input_shape=(60,)))
model_cat_3.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.2, input_shape=(30,)))
model_cat_3.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dropout(0.2, input_shape=(30,)))
model_cat_3.add(Dense(15,activation="sigmoid",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_3.add(Dense(9,activation="softmax"))
sgd = optimizers.SGD(lr = 0.01,momentum=0.3)
model_cat_3.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.CategoricalAccuracy())
t=time.time()
###################################################################
#
###################################################################
history_cat_3=model_cat_3.fit(X_train,k.utils.to_categorical(y_train),validation_data = (X_valid,k.utils.to_categorical(y_valid)),batch_size=100, epochs = 500, verbose = 1)
print("Total Time Taken is : ",t-time.time())
Epoch 1/500
WARNING:tensorflow:Layer batch_normalization_4 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
9/9 [==============================] - 1s 66ms/step - loss: 0.4664 - categorical_accuracy: 0.0072 - val_loss: 0.4658 - val_categorical_accuracy: 0.0125
Epoch 2/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4645 - categorical_accuracy: 0.0072 - val_loss: 0.4638 - val_categorical_accuracy: 0.0125
Epoch 3/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4626 - categorical_accuracy: 0.0072 - val_loss: 0.4619 - val_categorical_accuracy: 0.0125
Epoch 4/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4607 - categorical_accuracy: 0.0072 - val_loss: 0.4600 - val_categorical_accuracy: 0.0125
Epoch 5/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4588 - categorical_accuracy: 0.0072 - val_loss: 0.4582 - val_categorical_accuracy: 0.0125
Epoch 6/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4570 - categorical_accuracy: 0.0072 - val_loss: 0.4563 - val_categorical_accuracy: 0.0125
Epoch 7/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4551 - categorical_accuracy: 0.0072 - val_loss: 0.4544 - val_categorical_accuracy: 0.0125
Epoch 8/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4532 - categorical_accuracy: 0.0072 - val_loss: 0.4526 - val_categorical_accuracy: 0.0125
Epoch 9/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4514 - categorical_accuracy: 0.0072 - val_loss: 0.4507 - val_categorical_accuracy: 0.0125
Epoch 10/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4495 - categorical_accuracy: 0.0072 - val_loss: 0.4489 - val_categorical_accuracy: 0.0125
Epoch 11/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4477 - categorical_accuracy: 0.0072 - val_loss: 0.4471 - val_categorical_accuracy: 0.0125
Epoch 12/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4459 - categorical_accuracy: 0.0072 - val_loss: 0.4452 - val_categorical_accuracy: 0.0125
Epoch 13/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4440 - categorical_accuracy: 0.0072 - val_loss: 0.4434 - val_categorical_accuracy: 0.0125
Epoch 14/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4423 - categorical_accuracy: 0.0072 - val_loss: 0.4416 - val_categorical_accuracy: 0.0125
Epoch 15/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4405 - categorical_accuracy: 0.0072 - val_loss: 0.4398 - val_categorical_accuracy: 0.0125
Epoch 16/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4387 - categorical_accuracy: 0.0072 - val_loss: 0.4380 - val_categorical_accuracy: 0.0125
Epoch 17/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4369 - categorical_accuracy: 0.0072 - val_loss: 0.4363 - val_categorical_accuracy: 0.0125
Epoch 18/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4351 - categorical_accuracy: 0.0072 - val_loss: 0.4345 - val_categorical_accuracy: 0.0125
Epoch 19/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4334 - categorical_accuracy: 0.0072 - val_loss: 0.4327 - val_categorical_accuracy: 0.0125
Epoch 20/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4316 - categorical_accuracy: 0.0072 - val_loss: 0.4310 - val_categorical_accuracy: 0.0125
Epoch 21/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4299 - categorical_accuracy: 0.0072 - val_loss: 0.4293 - val_categorical_accuracy: 0.0125
Epoch 22/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4281 - categorical_accuracy: 0.0072 - val_loss: 0.4275 - val_categorical_accuracy: 0.0125
Epoch 23/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4264 - categorical_accuracy: 0.0072 - val_loss: 0.4258 - val_categorical_accuracy: 0.0125
Epoch 24/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4247 - categorical_accuracy: 0.0072 - val_loss: 0.4241 - val_categorical_accuracy: 0.0125
Epoch 25/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4230 - categorical_accuracy: 0.0072 - val_loss: 0.4224 - val_categorical_accuracy: 0.0125
Epoch 26/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4213 - categorical_accuracy: 0.0072 - val_loss: 0.4207 - val_categorical_accuracy: 0.0125
Epoch 27/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4196 - categorical_accuracy: 0.0072 - val_loss: 0.4190 - val_categorical_accuracy: 0.0125
Epoch 28/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4179 - categorical_accuracy: 0.0072 - val_loss: 0.4173 - val_categorical_accuracy: 0.0125
Epoch 29/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4162 - categorical_accuracy: 0.0072 - val_loss: 0.4156 - val_categorical_accuracy: 0.0125
Epoch 30/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4145 - categorical_accuracy: 0.0072 - val_loss: 0.4140 - val_categorical_accuracy: 0.0125
Epoch 31/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4129 - categorical_accuracy: 0.0072 - val_loss: 0.4123 - val_categorical_accuracy: 0.0125
Epoch 32/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4112 - categorical_accuracy: 0.0072 - val_loss: 0.4107 - val_categorical_accuracy: 0.0125
Epoch 33/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4096 - categorical_accuracy: 0.0072 - val_loss: 0.4090 - val_categorical_accuracy: 0.0125
Epoch 34/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4080 - categorical_accuracy: 0.0072 - val_loss: 0.4074 - val_categorical_accuracy: 0.0125
Epoch 35/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4063 - categorical_accuracy: 0.0072 - val_loss: 0.4058 - val_categorical_accuracy: 0.0125
Epoch 36/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4047 - categorical_accuracy: 0.0072 - val_loss: 0.4041 - val_categorical_accuracy: 0.0125
Epoch 37/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4031 - categorical_accuracy: 0.0072 - val_loss: 0.4025 - val_categorical_accuracy: 0.0125
Epoch 38/500
9/9 [==============================] - 0s 3ms/step - loss: 0.4015 - categorical_accuracy: 0.0072 - val_loss: 0.4009 - val_categorical_accuracy: 0.0125
Epoch 39/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3999 - categorical_accuracy: 0.0072 - val_loss: 0.3993 - val_categorical_accuracy: 0.0125
Epoch 40/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3983 - categorical_accuracy: 0.0072 - val_loss: 0.3978 - val_categorical_accuracy: 0.0125
Epoch 41/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3967 - categorical_accuracy: 0.0072 - val_loss: 0.3962 - val_categorical_accuracy: 0.0125
Epoch 42/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3952 - categorical_accuracy: 0.0072 - val_loss: 0.3946 - val_categorical_accuracy: 0.0125
Epoch 43/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3936 - categorical_accuracy: 0.0072 - val_loss: 0.3930 - val_categorical_accuracy: 0.0125
Epoch 44/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3920 - categorical_accuracy: 0.0072 - val_loss: 0.3915 - val_categorical_accuracy: 0.0125
Epoch 45/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3905 - categorical_accuracy: 0.0072 - val_loss: 0.3899 - val_categorical_accuracy: 0.0125
Epoch 46/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3889 - categorical_accuracy: 0.0072 - val_loss: 0.3884 - val_categorical_accuracy: 0.0125
Epoch 47/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3874 - categorical_accuracy: 0.0060 - val_loss: 0.3869 - val_categorical_accuracy: 0.0125
Epoch 48/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3859 - categorical_accuracy: 0.0107 - val_loss: 0.3853 - val_categorical_accuracy: 0.0125
Epoch 49/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3843 - categorical_accuracy: 0.0167 - val_loss: 0.3838 - val_categorical_accuracy: 0.0125
Epoch 50/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3828 - categorical_accuracy: 0.0155 - val_loss: 0.3823 - val_categorical_accuracy: 0.0125
Epoch 51/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3813 - categorical_accuracy: 0.0107 - val_loss: 0.3808 - val_categorical_accuracy: 0.0125
Epoch 52/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3799 - categorical_accuracy: 0.0143 - val_loss: 0.3793 - val_categorical_accuracy: 0.0125
Epoch 53/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3783 - categorical_accuracy: 0.0215 - val_loss: 0.3778 - val_categorical_accuracy: 0.0125
Epoch 54/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3769 - categorical_accuracy: 0.0226 - val_loss: 0.3763 - val_categorical_accuracy: 0.0125
Epoch 55/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3754 - categorical_accuracy: 0.0334 - val_loss: 0.3748 - val_categorical_accuracy: 0.0125
Epoch 56/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3739 - categorical_accuracy: 0.0346 - val_loss: 0.3734 - val_categorical_accuracy: 0.0125
Epoch 57/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3725 - categorical_accuracy: 0.0334 - val_loss: 0.3719 - val_categorical_accuracy: 0.0125
Epoch 58/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3710 - categorical_accuracy: 0.0501 - val_loss: 0.3705 - val_categorical_accuracy: 0.0125
Epoch 59/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3696 - categorical_accuracy: 0.0477 - val_loss: 0.3690 - val_categorical_accuracy: 0.0125
Epoch 60/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3681 - categorical_accuracy: 0.0584 - val_loss: 0.3676 - val_categorical_accuracy: 0.0125
Epoch 61/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3667 - categorical_accuracy: 0.0596 - val_loss: 0.3661 - val_categorical_accuracy: 0.1000
Epoch 62/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3653 - categorical_accuracy: 0.0715 - val_loss: 0.3647 - val_categorical_accuracy: 0.1000
Epoch 63/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3638 - categorical_accuracy: 0.0882 - val_loss: 0.3633 - val_categorical_accuracy: 0.1000
Epoch 64/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3624 - categorical_accuracy: 0.0894 - val_loss: 0.3619 - val_categorical_accuracy: 0.1000
Epoch 65/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3610 - categorical_accuracy: 0.1025 - val_loss: 0.3605 - val_categorical_accuracy: 0.1000
Epoch 66/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3596 - categorical_accuracy: 0.1061 - val_loss: 0.3591 - val_categorical_accuracy: 0.1000
Epoch 67/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3582 - categorical_accuracy: 0.1156 - val_loss: 0.3577 - val_categorical_accuracy: 0.1000
Epoch 68/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3568 - categorical_accuracy: 0.1204 - val_loss: 0.3563 - val_categorical_accuracy: 0.1000
Epoch 69/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3554 - categorical_accuracy: 0.1168 - val_loss: 0.3549 - val_categorical_accuracy: 0.1000
Epoch 70/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3541 - categorical_accuracy: 0.1204 - val_loss: 0.3535 - val_categorical_accuracy: 0.1000
Epoch 71/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3527 - categorical_accuracy: 0.1251 - val_loss: 0.3521 - val_categorical_accuracy: 0.1000
Epoch 72/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3513 - categorical_accuracy: 0.1299 - val_loss: 0.3508 - val_categorical_accuracy: 0.1000
Epoch 73/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3500 - categorical_accuracy: 0.1311 - val_loss: 0.3494 - val_categorical_accuracy: 0.1000
Epoch 74/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3486 - categorical_accuracy: 0.1299 - val_loss: 0.3481 - val_categorical_accuracy: 0.1000
Epoch 75/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3473 - categorical_accuracy: 0.1323 - val_loss: 0.3467 - val_categorical_accuracy: 0.1000
Epoch 76/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3459 - categorical_accuracy: 0.1347 - val_loss: 0.3454 - val_categorical_accuracy: 0.1000
Epoch 77/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3446 - categorical_accuracy: 0.1323 - val_loss: 0.3441 - val_categorical_accuracy: 0.1000
Epoch 78/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3433 - categorical_accuracy: 0.1335 - val_loss: 0.3427 - val_categorical_accuracy: 0.1000
Epoch 79/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3420 - categorical_accuracy: 0.1347 - val_loss: 0.3414 - val_categorical_accuracy: 0.1000
Epoch 80/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3407 - categorical_accuracy: 0.1347 - val_loss: 0.3401 - val_categorical_accuracy: 0.1000
Epoch 81/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3394 - categorical_accuracy: 0.1347 - val_loss: 0.3388 - val_categorical_accuracy: 0.1000
Epoch 82/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3381 - categorical_accuracy: 0.1347 - val_loss: 0.3375 - val_categorical_accuracy: 0.1000
Epoch 83/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3368 - categorical_accuracy: 0.1347 - val_loss: 0.3362 - val_categorical_accuracy: 0.1000
Epoch 84/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3355 - categorical_accuracy: 0.1347 - val_loss: 0.3349 - val_categorical_accuracy: 0.1000
Epoch 85/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3342 - categorical_accuracy: 0.1347 - val_loss: 0.3336 - val_categorical_accuracy: 0.1000
Epoch 86/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3329 - categorical_accuracy: 0.1347 - val_loss: 0.3324 - val_categorical_accuracy: 0.1000
Epoch 87/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3316 - categorical_accuracy: 0.1347 - val_loss: 0.3311 - val_categorical_accuracy: 0.1000
Epoch 88/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3304 - categorical_accuracy: 0.1347 - val_loss: 0.3298 - val_categorical_accuracy: 0.1000
Epoch 89/500
9/9 [==============================] - 0s 2ms/step - loss: 0.3291 - categorical_accuracy: 0.1347 - val_loss: 0.3286 - val_categorical_accuracy: 0.1000
Epoch 90/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3279 - categorical_accuracy: 0.1347 - val_loss: 0.3273 - val_categorical_accuracy: 0.1000
Epoch 91/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3266 - categorical_accuracy: 0.1347 - val_loss: 0.3261 - val_categorical_accuracy: 0.1000
Epoch 92/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3254 - categorical_accuracy: 0.1347 - val_loss: 0.3248 - val_categorical_accuracy: 0.1000
Epoch 93/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3242 - categorical_accuracy: 0.1347 - val_loss: 0.3236 - val_categorical_accuracy: 0.1000
Epoch 94/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3229 - categorical_accuracy: 0.1347 - val_loss: 0.3223 - val_categorical_accuracy: 0.1000
Epoch 95/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3217 - categorical_accuracy: 0.1347 - val_loss: 0.3211 - val_categorical_accuracy: 0.1000
Epoch 96/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3205 - categorical_accuracy: 0.1347 - val_loss: 0.3199 - val_categorical_accuracy: 0.1000
Epoch 97/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3193 - categorical_accuracy: 0.1347 - val_loss: 0.3187 - val_categorical_accuracy: 0.1000
Epoch 98/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3181 - categorical_accuracy: 0.1347 - val_loss: 0.3175 - val_categorical_accuracy: 0.1000
Epoch 99/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3169 - categorical_accuracy: 0.1347 - val_loss: 0.3163 - val_categorical_accuracy: 0.1000
Epoch 100/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3157 - categorical_accuracy: 0.1347 - val_loss: 0.3151 - val_categorical_accuracy: 0.1000
Epoch 101/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3145 - categorical_accuracy: 0.1347 - val_loss: 0.3139 - val_categorical_accuracy: 0.1000
Epoch 102/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3133 - categorical_accuracy: 0.1347 - val_loss: 0.3127 - val_categorical_accuracy: 0.1000
Epoch 103/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3122 - categorical_accuracy: 0.1347 - val_loss: 0.3115 - val_categorical_accuracy: 0.1000
Epoch 104/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3110 - categorical_accuracy: 0.1347 - val_loss: 0.3104 - val_categorical_accuracy: 0.1000
Epoch 105/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3098 - categorical_accuracy: 0.1347 - val_loss: 0.3092 - val_categorical_accuracy: 0.1000
Epoch 106/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3086 - categorical_accuracy: 0.1347 - val_loss: 0.3080 - val_categorical_accuracy: 0.1000
Epoch 107/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3075 - categorical_accuracy: 0.1347 - val_loss: 0.3069 - val_categorical_accuracy: 0.1000
Epoch 108/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3063 - categorical_accuracy: 0.1347 - val_loss: 0.3057 - val_categorical_accuracy: 0.1000
Epoch 109/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3052 - categorical_accuracy: 0.1347 - val_loss: 0.3046 - val_categorical_accuracy: 0.1000
Epoch 110/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3041 - categorical_accuracy: 0.1347 - val_loss: 0.3034 - val_categorical_accuracy: 0.1000
Epoch 111/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3029 - categorical_accuracy: 0.1347 - val_loss: 0.3023 - val_categorical_accuracy: 0.1000
Epoch 112/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3018 - categorical_accuracy: 0.1347 - val_loss: 0.3011 - val_categorical_accuracy: 0.1000
Epoch 113/500
9/9 [==============================] - 0s 3ms/step - loss: 0.3007 - categorical_accuracy: 0.1347 - val_loss: 0.3000 - val_categorical_accuracy: 0.1000
Epoch 114/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2995 - categorical_accuracy: 0.1347 - val_loss: 0.2989 - val_categorical_accuracy: 0.1000
Epoch 115/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2984 - categorical_accuracy: 0.1347 - val_loss: 0.2978 - val_categorical_accuracy: 0.1000
Epoch 116/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2974 - categorical_accuracy: 0.1347 - val_loss: 0.2967 - val_categorical_accuracy: 0.1000
Epoch 117/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2962 - categorical_accuracy: 0.1347 - val_loss: 0.2956 - val_categorical_accuracy: 0.1000
Epoch 118/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2951 - categorical_accuracy: 0.1347 - val_loss: 0.2944 - val_categorical_accuracy: 0.1000
Epoch 119/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2940 - categorical_accuracy: 0.1347 - val_loss: 0.2934 - val_categorical_accuracy: 0.1000
Epoch 120/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2930 - categorical_accuracy: 0.1347 - val_loss: 0.2923 - val_categorical_accuracy: 0.1000
Epoch 121/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2919 - categorical_accuracy: 0.1347 - val_loss: 0.2912 - val_categorical_accuracy: 0.1000
Epoch 122/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2908 - categorical_accuracy: 0.1347 - val_loss: 0.2901 - val_categorical_accuracy: 0.1000
Epoch 123/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2897 - categorical_accuracy: 0.1347 - val_loss: 0.2890 - val_categorical_accuracy: 0.1000
Epoch 124/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2887 - categorical_accuracy: 0.1347 - val_loss: 0.2879 - val_categorical_accuracy: 0.1000
Epoch 125/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2876 - categorical_accuracy: 0.1347 - val_loss: 0.2869 - val_categorical_accuracy: 0.1000
Epoch 126/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2865 - categorical_accuracy: 0.1347 - val_loss: 0.2858 - val_categorical_accuracy: 0.1000
Epoch 127/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2855 - categorical_accuracy: 0.1347 - val_loss: 0.2848 - val_categorical_accuracy: 0.1000
Epoch 128/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2845 - categorical_accuracy: 0.1347 - val_loss: 0.2837 - val_categorical_accuracy: 0.1000
Epoch 129/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2834 - categorical_accuracy: 0.1418 - val_loss: 0.2827 - val_categorical_accuracy: 0.1000
Epoch 130/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2824 - categorical_accuracy: 0.1418 - val_loss: 0.2816 - val_categorical_accuracy: 0.1000
Epoch 131/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2813 - categorical_accuracy: 0.1442 - val_loss: 0.2806 - val_categorical_accuracy: 0.1000
Epoch 132/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2803 - categorical_accuracy: 0.1633 - val_loss: 0.2795 - val_categorical_accuracy: 0.1000
Epoch 133/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2793 - categorical_accuracy: 0.2002 - val_loss: 0.2785 - val_categorical_accuracy: 0.1000
Epoch 134/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2783 - categorical_accuracy: 0.2193 - val_loss: 0.2775 - val_categorical_accuracy: 0.1000
Epoch 135/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2773 - categorical_accuracy: 0.2503 - val_loss: 0.2765 - val_categorical_accuracy: 0.4250
Epoch 136/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2763 - categorical_accuracy: 0.2753 - val_loss: 0.2755 - val_categorical_accuracy: 0.4250
Epoch 137/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2752 - categorical_accuracy: 0.3004 - val_loss: 0.2745 - val_categorical_accuracy: 0.4250
Epoch 138/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2743 - categorical_accuracy: 0.3504 - val_loss: 0.2735 - val_categorical_accuracy: 0.4250
Epoch 139/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2733 - categorical_accuracy: 0.3719 - val_loss: 0.2725 - val_categorical_accuracy: 0.4250
Epoch 140/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2723 - categorical_accuracy: 0.3766 - val_loss: 0.2715 - val_categorical_accuracy: 0.4250
Epoch 141/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2713 - categorical_accuracy: 0.3897 - val_loss: 0.2705 - val_categorical_accuracy: 0.4250
Epoch 142/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2703 - categorical_accuracy: 0.3838 - val_loss: 0.2695 - val_categorical_accuracy: 0.4250
Epoch 143/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2693 - categorical_accuracy: 0.3862 - val_loss: 0.2685 - val_categorical_accuracy: 0.4250
Epoch 144/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2684 - categorical_accuracy: 0.3862 - val_loss: 0.2675 - val_categorical_accuracy: 0.4250
Epoch 145/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2674 - categorical_accuracy: 0.3862 - val_loss: 0.2666 - val_categorical_accuracy: 0.4250
Epoch 146/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2664 - categorical_accuracy: 0.3874 - val_loss: 0.2656 - val_categorical_accuracy: 0.4250
Epoch 147/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2655 - categorical_accuracy: 0.3874 - val_loss: 0.2646 - val_categorical_accuracy: 0.4250
Epoch 148/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2645 - categorical_accuracy: 0.3874 - val_loss: 0.2637 - val_categorical_accuracy: 0.4250
Epoch 149/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2636 - categorical_accuracy: 0.3874 - val_loss: 0.2627 - val_categorical_accuracy: 0.4250
Epoch 150/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2627 - categorical_accuracy: 0.3874 - val_loss: 0.2618 - val_categorical_accuracy: 0.4250
Epoch 151/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2617 - categorical_accuracy: 0.3874 - val_loss: 0.2608 - val_categorical_accuracy: 0.4250
Epoch 152/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2608 - categorical_accuracy: 0.3874 - val_loss: 0.2599 - val_categorical_accuracy: 0.4250
Epoch 153/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2598 - categorical_accuracy: 0.3874 - val_loss: 0.2590 - val_categorical_accuracy: 0.4250
Epoch 154/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2589 - categorical_accuracy: 0.3874 - val_loss: 0.2580 - val_categorical_accuracy: 0.4250
Epoch 155/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2580 - categorical_accuracy: 0.3874 - val_loss: 0.2571 - val_categorical_accuracy: 0.4250
Epoch 156/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2571 - categorical_accuracy: 0.3874 - val_loss: 0.2562 - val_categorical_accuracy: 0.4250
Epoch 157/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2562 - categorical_accuracy: 0.3874 - val_loss: 0.2553 - val_categorical_accuracy: 0.4250
Epoch 158/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2553 - categorical_accuracy: 0.3874 - val_loss: 0.2544 - val_categorical_accuracy: 0.4250
Epoch 159/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2544 - categorical_accuracy: 0.3874 - val_loss: 0.2534 - val_categorical_accuracy: 0.4250
Epoch 160/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2535 - categorical_accuracy: 0.3874 - val_loss: 0.2525 - val_categorical_accuracy: 0.4250
Epoch 161/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2526 - categorical_accuracy: 0.3874 - val_loss: 0.2516 - val_categorical_accuracy: 0.4250
Epoch 162/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2517 - categorical_accuracy: 0.3874 - val_loss: 0.2507 - val_categorical_accuracy: 0.4250
Epoch 163/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2508 - categorical_accuracy: 0.3874 - val_loss: 0.2499 - val_categorical_accuracy: 0.4250
Epoch 164/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2499 - categorical_accuracy: 0.3874 - val_loss: 0.2490 - val_categorical_accuracy: 0.4250
Epoch 165/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2491 - categorical_accuracy: 0.3874 - val_loss: 0.2481 - val_categorical_accuracy: 0.4250
Epoch 166/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2482 - categorical_accuracy: 0.3874 - val_loss: 0.2472 - val_categorical_accuracy: 0.4250
Epoch 167/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2473 - categorical_accuracy: 0.3874 - val_loss: 0.2463 - val_categorical_accuracy: 0.4250
Epoch 168/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2465 - categorical_accuracy: 0.3874 - val_loss: 0.2455 - val_categorical_accuracy: 0.4250
Epoch 169/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2456 - categorical_accuracy: 0.3874 - val_loss: 0.2446 - val_categorical_accuracy: 0.4250
Epoch 170/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2448 - categorical_accuracy: 0.3874 - val_loss: 0.2437 - val_categorical_accuracy: 0.4250
Epoch 171/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2439 - categorical_accuracy: 0.3874 - val_loss: 0.2429 - val_categorical_accuracy: 0.4250
Epoch 172/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2430 - categorical_accuracy: 0.3874 - val_loss: 0.2420 - val_categorical_accuracy: 0.4250
Epoch 173/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2422 - categorical_accuracy: 0.3874 - val_loss: 0.2412 - val_categorical_accuracy: 0.4250
Epoch 174/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2414 - categorical_accuracy: 0.3874 - val_loss: 0.2403 - val_categorical_accuracy: 0.4250
Epoch 175/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2405 - categorical_accuracy: 0.3874 - val_loss: 0.2395 - val_categorical_accuracy: 0.4250
Epoch 176/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2397 - categorical_accuracy: 0.3874 - val_loss: 0.2387 - val_categorical_accuracy: 0.4250
Epoch 177/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2389 - categorical_accuracy: 0.3874 - val_loss: 0.2378 - val_categorical_accuracy: 0.4250
Epoch 178/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2381 - categorical_accuracy: 0.3874 - val_loss: 0.2370 - val_categorical_accuracy: 0.4250
Epoch 179/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2372 - categorical_accuracy: 0.3874 - val_loss: 0.2362 - val_categorical_accuracy: 0.4250
Epoch 180/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2364 - categorical_accuracy: 0.3874 - val_loss: 0.2354 - val_categorical_accuracy: 0.4250
Epoch 181/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2356 - categorical_accuracy: 0.3874 - val_loss: 0.2346 - val_categorical_accuracy: 0.4250
Epoch 182/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2348 - categorical_accuracy: 0.3874 - val_loss: 0.2337 - val_categorical_accuracy: 0.4250
Epoch 183/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2340 - categorical_accuracy: 0.3874 - val_loss: 0.2329 - val_categorical_accuracy: 0.4250
Epoch 184/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2332 - categorical_accuracy: 0.3874 - val_loss: 0.2321 - val_categorical_accuracy: 0.4250
Epoch 185/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2324 - categorical_accuracy: 0.3874 - val_loss: 0.2313 - val_categorical_accuracy: 0.4250
Epoch 186/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2317 - categorical_accuracy: 0.3874 - val_loss: 0.2305 - val_categorical_accuracy: 0.4250
Epoch 187/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2309 - categorical_accuracy: 0.3874 - val_loss: 0.2297 - val_categorical_accuracy: 0.4250
Epoch 188/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2301 - categorical_accuracy: 0.3874 - val_loss: 0.2290 - val_categorical_accuracy: 0.4250
Epoch 189/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2293 - categorical_accuracy: 0.3874 - val_loss: 0.2282 - val_categorical_accuracy: 0.4250
Epoch 190/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2286 - categorical_accuracy: 0.3874 - val_loss: 0.2274 - val_categorical_accuracy: 0.4250
Epoch 191/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2278 - categorical_accuracy: 0.3874 - val_loss: 0.2266 - val_categorical_accuracy: 0.4250
Epoch 192/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2270 - categorical_accuracy: 0.3874 - val_loss: 0.2259 - val_categorical_accuracy: 0.4250
Epoch 193/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2262 - categorical_accuracy: 0.3874 - val_loss: 0.2251 - val_categorical_accuracy: 0.4250
Epoch 194/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2255 - categorical_accuracy: 0.3874 - val_loss: 0.2243 - val_categorical_accuracy: 0.4250
Epoch 195/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2248 - categorical_accuracy: 0.3874 - val_loss: 0.2236 - val_categorical_accuracy: 0.4250
Epoch 196/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2240 - categorical_accuracy: 0.3874 - val_loss: 0.2228 - val_categorical_accuracy: 0.4250
Epoch 197/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2232 - categorical_accuracy: 0.3874 - val_loss: 0.2221 - val_categorical_accuracy: 0.4250
Epoch 198/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2225 - categorical_accuracy: 0.3874 - val_loss: 0.2213 - val_categorical_accuracy: 0.4250
Epoch 199/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2218 - categorical_accuracy: 0.3874 - val_loss: 0.2206 - val_categorical_accuracy: 0.4250
Epoch 200/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2210 - categorical_accuracy: 0.3874 - val_loss: 0.2198 - val_categorical_accuracy: 0.4250
Epoch 201/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2203 - categorical_accuracy: 0.3874 - val_loss: 0.2191 - val_categorical_accuracy: 0.4250
Epoch 202/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2195 - categorical_accuracy: 0.3874 - val_loss: 0.2183 - val_categorical_accuracy: 0.4250
Epoch 203/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2188 - categorical_accuracy: 0.3874 - val_loss: 0.2176 - val_categorical_accuracy: 0.4250
Epoch 204/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2181 - categorical_accuracy: 0.3874 - val_loss: 0.2169 - val_categorical_accuracy: 0.4250
Epoch 205/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2174 - categorical_accuracy: 0.3874 - val_loss: 0.2162 - val_categorical_accuracy: 0.4250
Epoch 206/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2167 - categorical_accuracy: 0.3874 - val_loss: 0.2155 - val_categorical_accuracy: 0.4250
Epoch 207/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2160 - categorical_accuracy: 0.3874 - val_loss: 0.2147 - val_categorical_accuracy: 0.4250
Epoch 208/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2153 - categorical_accuracy: 0.3874 - val_loss: 0.2140 - val_categorical_accuracy: 0.4250
Epoch 209/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2146 - categorical_accuracy: 0.3874 - val_loss: 0.2133 - val_categorical_accuracy: 0.4250
Epoch 210/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2139 - categorical_accuracy: 0.3874 - val_loss: 0.2126 - val_categorical_accuracy: 0.4250
Epoch 211/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2132 - categorical_accuracy: 0.3874 - val_loss: 0.2119 - val_categorical_accuracy: 0.4250
Epoch 212/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2125 - categorical_accuracy: 0.3874 - val_loss: 0.2112 - val_categorical_accuracy: 0.4250
Epoch 213/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2118 - categorical_accuracy: 0.3874 - val_loss: 0.2105 - val_categorical_accuracy: 0.4250
Epoch 214/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2111 - categorical_accuracy: 0.3874 - val_loss: 0.2098 - val_categorical_accuracy: 0.4250
Epoch 215/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2105 - categorical_accuracy: 0.3874 - val_loss: 0.2091 - val_categorical_accuracy: 0.4250
Epoch 216/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2098 - categorical_accuracy: 0.3874 - val_loss: 0.2085 - val_categorical_accuracy: 0.4250
Epoch 217/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2091 - categorical_accuracy: 0.3874 - val_loss: 0.2078 - val_categorical_accuracy: 0.4250
Epoch 218/500
9/9 [==============================] - 0s 2ms/step - loss: 0.2085 - categorical_accuracy: 0.3874 - val_loss: 0.2071 - val_categorical_accuracy: 0.4250
Epoch 219/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2078 - categorical_accuracy: 0.3874 - val_loss: 0.2064 - val_categorical_accuracy: 0.4250
Epoch 220/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2072 - categorical_accuracy: 0.3874 - val_loss: 0.2058 - val_categorical_accuracy: 0.4250
Epoch 221/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2065 - categorical_accuracy: 0.3874 - val_loss: 0.2051 - val_categorical_accuracy: 0.4250
Epoch 222/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2058 - categorical_accuracy: 0.3874 - val_loss: 0.2044 - val_categorical_accuracy: 0.4250
Epoch 223/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2052 - categorical_accuracy: 0.3874 - val_loss: 0.2038 - val_categorical_accuracy: 0.4250
Epoch 224/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2045 - categorical_accuracy: 0.3874 - val_loss: 0.2031 - val_categorical_accuracy: 0.4250
Epoch 225/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2039 - categorical_accuracy: 0.3874 - val_loss: 0.2025 - val_categorical_accuracy: 0.4250
Epoch 226/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2032 - categorical_accuracy: 0.3874 - val_loss: 0.2018 - val_categorical_accuracy: 0.4250
Epoch 227/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2026 - categorical_accuracy: 0.3874 - val_loss: 0.2012 - val_categorical_accuracy: 0.4250
Epoch 228/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2019 - categorical_accuracy: 0.3874 - val_loss: 0.2005 - val_categorical_accuracy: 0.4250
Epoch 229/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2013 - categorical_accuracy: 0.3874 - val_loss: 0.1999 - val_categorical_accuracy: 0.4250
Epoch 230/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2007 - categorical_accuracy: 0.3874 - val_loss: 0.1993 - val_categorical_accuracy: 0.4250
Epoch 231/500
9/9 [==============================] - 0s 3ms/step - loss: 0.2001 - categorical_accuracy: 0.3874 - val_loss: 0.1986 - val_categorical_accuracy: 0.4250
Epoch 232/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1994 - categorical_accuracy: 0.3874 - val_loss: 0.1980 - val_categorical_accuracy: 0.4250
Epoch 233/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1988 - categorical_accuracy: 0.3874 - val_loss: 0.1974 - val_categorical_accuracy: 0.4250
Epoch 234/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1982 - categorical_accuracy: 0.3874 - val_loss: 0.1968 - val_categorical_accuracy: 0.4250
Epoch 235/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1976 - categorical_accuracy: 0.3874 - val_loss: 0.1961 - val_categorical_accuracy: 0.4250
Epoch 236/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1970 - categorical_accuracy: 0.3874 - val_loss: 0.1955 - val_categorical_accuracy: 0.4250
Epoch 237/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1964 - categorical_accuracy: 0.3874 - val_loss: 0.1949 - val_categorical_accuracy: 0.4250
Epoch 238/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1958 - categorical_accuracy: 0.3874 - val_loss: 0.1943 - val_categorical_accuracy: 0.4250
Epoch 239/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1952 - categorical_accuracy: 0.3874 - val_loss: 0.1937 - val_categorical_accuracy: 0.4250
Epoch 240/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1946 - categorical_accuracy: 0.3874 - val_loss: 0.1931 - val_categorical_accuracy: 0.4250
Epoch 241/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1940 - categorical_accuracy: 0.3874 - val_loss: 0.1925 - val_categorical_accuracy: 0.4250
Epoch 242/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1934 - categorical_accuracy: 0.3874 - val_loss: 0.1919 - val_categorical_accuracy: 0.4250
Epoch 243/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1928 - categorical_accuracy: 0.3874 - val_loss: 0.1913 - val_categorical_accuracy: 0.4250
Epoch 244/500
9/9 [==============================] - ETA: 0s - loss: 0.1927 - categorical_accuracy: 0.38 - 0s 3ms/step - loss: 0.1922 - categorical_accuracy: 0.3874 - val_loss: 0.1907 - val_categorical_accuracy: 0.4250
Epoch 245/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1916 - categorical_accuracy: 0.3874 - val_loss: 0.1901 - val_categorical_accuracy: 0.4250
Epoch 246/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1911 - categorical_accuracy: 0.3874 - val_loss: 0.1895 - val_categorical_accuracy: 0.4250
Epoch 247/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1905 - categorical_accuracy: 0.3874 - val_loss: 0.1889 - val_categorical_accuracy: 0.4250
Epoch 248/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1899 - categorical_accuracy: 0.3874 - val_loss: 0.1884 - val_categorical_accuracy: 0.4250
Epoch 249/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1893 - categorical_accuracy: 0.3874 - val_loss: 0.1878 - val_categorical_accuracy: 0.4250
Epoch 250/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1888 - categorical_accuracy: 0.3874 - val_loss: 0.1872 - val_categorical_accuracy: 0.4250
Epoch 251/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1882 - categorical_accuracy: 0.3874 - val_loss: 0.1866 - val_categorical_accuracy: 0.4250
Epoch 252/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1876 - categorical_accuracy: 0.3874 - val_loss: 0.1861 - val_categorical_accuracy: 0.4250
Epoch 253/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1871 - categorical_accuracy: 0.3874 - val_loss: 0.1855 - val_categorical_accuracy: 0.4250
Epoch 254/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1865 - categorical_accuracy: 0.3874 - val_loss: 0.1849 - val_categorical_accuracy: 0.4250
Epoch 255/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1859 - categorical_accuracy: 0.3874 - val_loss: 0.1844 - val_categorical_accuracy: 0.4250
Epoch 256/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1854 - categorical_accuracy: 0.3874 - val_loss: 0.1838 - val_categorical_accuracy: 0.4250
Epoch 257/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1849 - categorical_accuracy: 0.3874 - val_loss: 0.1833 - val_categorical_accuracy: 0.4250
Epoch 258/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1843 - categorical_accuracy: 0.3874 - val_loss: 0.1827 - val_categorical_accuracy: 0.4250
Epoch 259/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1838 - categorical_accuracy: 0.3874 - val_loss: 0.1822 - val_categorical_accuracy: 0.4250
Epoch 260/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1833 - categorical_accuracy: 0.3874 - val_loss: 0.1816 - val_categorical_accuracy: 0.4250
Epoch 261/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1827 - categorical_accuracy: 0.3874 - val_loss: 0.1811 - val_categorical_accuracy: 0.4250
Epoch 262/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1822 - categorical_accuracy: 0.3874 - val_loss: 0.1806 - val_categorical_accuracy: 0.4250
Epoch 263/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1816 - categorical_accuracy: 0.3874 - val_loss: 0.1800 - val_categorical_accuracy: 0.4250
Epoch 264/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1811 - categorical_accuracy: 0.3874 - val_loss: 0.1795 - val_categorical_accuracy: 0.4250
Epoch 265/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1806 - categorical_accuracy: 0.3874 - val_loss: 0.1790 - val_categorical_accuracy: 0.4250
Epoch 266/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1801 - categorical_accuracy: 0.3874 - val_loss: 0.1784 - val_categorical_accuracy: 0.4250
Epoch 267/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1796 - categorical_accuracy: 0.3874 - val_loss: 0.1779 - val_categorical_accuracy: 0.4250
Epoch 268/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1790 - categorical_accuracy: 0.3874 - val_loss: 0.1774 - val_categorical_accuracy: 0.4250
Epoch 269/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1785 - categorical_accuracy: 0.3874 - val_loss: 0.1769 - val_categorical_accuracy: 0.4250
Epoch 270/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1780 - categorical_accuracy: 0.3874 - val_loss: 0.1763 - val_categorical_accuracy: 0.4250
Epoch 271/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1775 - categorical_accuracy: 0.3874 - val_loss: 0.1758 - val_categorical_accuracy: 0.4250
Epoch 272/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1769 - categorical_accuracy: 0.3874 - val_loss: 0.1753 - val_categorical_accuracy: 0.4250
Epoch 273/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1765 - categorical_accuracy: 0.3874 - val_loss: 0.1748 - val_categorical_accuracy: 0.4250
Epoch 274/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1760 - categorical_accuracy: 0.3874 - val_loss: 0.1743 - val_categorical_accuracy: 0.4250
Epoch 275/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1754 - categorical_accuracy: 0.3874 - val_loss: 0.1738 - val_categorical_accuracy: 0.4250
Epoch 276/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1750 - categorical_accuracy: 0.3874 - val_loss: 0.1733 - val_categorical_accuracy: 0.4250
Epoch 277/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1744 - categorical_accuracy: 0.3874 - val_loss: 0.1728 - val_categorical_accuracy: 0.4250
Epoch 278/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1740 - categorical_accuracy: 0.3874 - val_loss: 0.1723 - val_categorical_accuracy: 0.4250
Epoch 279/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1735 - categorical_accuracy: 0.3874 - val_loss: 0.1718 - val_categorical_accuracy: 0.4250
Epoch 280/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1730 - categorical_accuracy: 0.3874 - val_loss: 0.1713 - val_categorical_accuracy: 0.4250
Epoch 281/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1725 - categorical_accuracy: 0.3874 - val_loss: 0.1708 - val_categorical_accuracy: 0.4250
Epoch 282/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1720 - categorical_accuracy: 0.3874 - val_loss: 0.1703 - val_categorical_accuracy: 0.4250
Epoch 283/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1715 - categorical_accuracy: 0.3874 - val_loss: 0.1698 - val_categorical_accuracy: 0.4250
Epoch 284/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1710 - categorical_accuracy: 0.3874 - val_loss: 0.1694 - val_categorical_accuracy: 0.4250
Epoch 285/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1706 - categorical_accuracy: 0.3874 - val_loss: 0.1689 - val_categorical_accuracy: 0.4250
Epoch 286/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1701 - categorical_accuracy: 0.3874 - val_loss: 0.1684 - val_categorical_accuracy: 0.4250
Epoch 287/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1696 - categorical_accuracy: 0.3874 - val_loss: 0.1679 - val_categorical_accuracy: 0.4250
Epoch 288/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1692 - categorical_accuracy: 0.3874 - val_loss: 0.1674 - val_categorical_accuracy: 0.4250
Epoch 289/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1687 - categorical_accuracy: 0.3874 - val_loss: 0.1670 - val_categorical_accuracy: 0.4250
Epoch 290/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1682 - categorical_accuracy: 0.3874 - val_loss: 0.1665 - val_categorical_accuracy: 0.4250
Epoch 291/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1677 - categorical_accuracy: 0.3874 - val_loss: 0.1660 - val_categorical_accuracy: 0.4250
Epoch 292/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1673 - categorical_accuracy: 0.3874 - val_loss: 0.1656 - val_categorical_accuracy: 0.4250
Epoch 293/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1668 - categorical_accuracy: 0.3874 - val_loss: 0.1651 - val_categorical_accuracy: 0.4250
Epoch 294/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1664 - categorical_accuracy: 0.3874 - val_loss: 0.1646 - val_categorical_accuracy: 0.4250
Epoch 295/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1659 - categorical_accuracy: 0.3874 - val_loss: 0.1642 - val_categorical_accuracy: 0.4250
Epoch 296/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1655 - categorical_accuracy: 0.3874 - val_loss: 0.1637 - val_categorical_accuracy: 0.4250
Epoch 297/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1650 - categorical_accuracy: 0.3874 - val_loss: 0.1633 - val_categorical_accuracy: 0.4250
Epoch 298/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1645 - categorical_accuracy: 0.3874 - val_loss: 0.1628 - val_categorical_accuracy: 0.4250
Epoch 299/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1641 - categorical_accuracy: 0.3874 - val_loss: 0.1624 - val_categorical_accuracy: 0.4250
Epoch 300/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1637 - categorical_accuracy: 0.3874 - val_loss: 0.1619 - val_categorical_accuracy: 0.4250
Epoch 301/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1632 - categorical_accuracy: 0.3874 - val_loss: 0.1615 - val_categorical_accuracy: 0.4250
Epoch 302/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1628 - categorical_accuracy: 0.3874 - val_loss: 0.1610 - val_categorical_accuracy: 0.4250
Epoch 303/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1624 - categorical_accuracy: 0.3874 - val_loss: 0.1606 - val_categorical_accuracy: 0.4250
Epoch 304/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1619 - categorical_accuracy: 0.3874 - val_loss: 0.1602 - val_categorical_accuracy: 0.4250
Epoch 305/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1615 - categorical_accuracy: 0.3874 - val_loss: 0.1597 - val_categorical_accuracy: 0.4250
Epoch 306/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1610 - categorical_accuracy: 0.3874 - val_loss: 0.1593 - val_categorical_accuracy: 0.4250
Epoch 307/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1606 - categorical_accuracy: 0.3874 - val_loss: 0.1589 - val_categorical_accuracy: 0.4250
Epoch 308/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1602 - categorical_accuracy: 0.3874 - val_loss: 0.1584 - val_categorical_accuracy: 0.4250
Epoch 309/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1598 - categorical_accuracy: 0.3874 - val_loss: 0.1580 - val_categorical_accuracy: 0.4250
Epoch 310/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1594 - categorical_accuracy: 0.3874 - val_loss: 0.1576 - val_categorical_accuracy: 0.4250
Epoch 311/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1589 - categorical_accuracy: 0.3874 - val_loss: 0.1571 - val_categorical_accuracy: 0.4250
Epoch 312/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1585 - categorical_accuracy: 0.3874 - val_loss: 0.1567 - val_categorical_accuracy: 0.4250
Epoch 313/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1581 - categorical_accuracy: 0.3874 - val_loss: 0.1563 - val_categorical_accuracy: 0.4250
Epoch 314/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1577 - categorical_accuracy: 0.3874 - val_loss: 0.1559 - val_categorical_accuracy: 0.4250
Epoch 315/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1573 - categorical_accuracy: 0.3874 - val_loss: 0.1555 - val_categorical_accuracy: 0.4250
Epoch 316/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1568 - categorical_accuracy: 0.3874 - val_loss: 0.1551 - val_categorical_accuracy: 0.4250
Epoch 317/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1564 - categorical_accuracy: 0.3874 - val_loss: 0.1546 - val_categorical_accuracy: 0.4250
Epoch 318/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1560 - categorical_accuracy: 0.3874 - val_loss: 0.1542 - val_categorical_accuracy: 0.4250
Epoch 319/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1556 - categorical_accuracy: 0.3874 - val_loss: 0.1538 - val_categorical_accuracy: 0.4250
Epoch 320/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1552 - categorical_accuracy: 0.3874 - val_loss: 0.1534 - val_categorical_accuracy: 0.4250
Epoch 321/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1548 - categorical_accuracy: 0.3874 - val_loss: 0.1530 - val_categorical_accuracy: 0.4250
Epoch 322/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1544 - categorical_accuracy: 0.3874 - val_loss: 0.1526 - val_categorical_accuracy: 0.4250
Epoch 323/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1540 - categorical_accuracy: 0.3874 - val_loss: 0.1522 - val_categorical_accuracy: 0.4250
Epoch 324/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1536 - categorical_accuracy: 0.3874 - val_loss: 0.1518 - val_categorical_accuracy: 0.4250
Epoch 325/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1532 - categorical_accuracy: 0.3874 - val_loss: 0.1514 - val_categorical_accuracy: 0.4250
Epoch 326/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1528 - categorical_accuracy: 0.3874 - val_loss: 0.1510 - val_categorical_accuracy: 0.4250
Epoch 327/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1524 - categorical_accuracy: 0.3874 - val_loss: 0.1506 - val_categorical_accuracy: 0.4250
Epoch 328/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1520 - categorical_accuracy: 0.3874 - val_loss: 0.1502 - val_categorical_accuracy: 0.4250
Epoch 329/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1516 - categorical_accuracy: 0.3874 - val_loss: 0.1498 - val_categorical_accuracy: 0.4250
Epoch 330/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1512 - categorical_accuracy: 0.3874 - val_loss: 0.1494 - val_categorical_accuracy: 0.4250
Epoch 331/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1509 - categorical_accuracy: 0.3874 - val_loss: 0.1490 - val_categorical_accuracy: 0.4250
Epoch 332/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1505 - categorical_accuracy: 0.3874 - val_loss: 0.1487 - val_categorical_accuracy: 0.4250
Epoch 333/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1501 - categorical_accuracy: 0.3874 - val_loss: 0.1483 - val_categorical_accuracy: 0.4250
Epoch 334/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1497 - categorical_accuracy: 0.3874 - val_loss: 0.1479 - val_categorical_accuracy: 0.4250
Epoch 335/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1493 - categorical_accuracy: 0.3874 - val_loss: 0.1475 - val_categorical_accuracy: 0.4250
Epoch 336/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1489 - categorical_accuracy: 0.3874 - val_loss: 0.1471 - val_categorical_accuracy: 0.4250
Epoch 337/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1485 - categorical_accuracy: 0.3874 - val_loss: 0.1468 - val_categorical_accuracy: 0.4250
Epoch 338/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1482 - categorical_accuracy: 0.3874 - val_loss: 0.1464 - val_categorical_accuracy: 0.4250
Epoch 339/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1478 - categorical_accuracy: 0.3874 - val_loss: 0.1460 - val_categorical_accuracy: 0.4250
Epoch 340/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1474 - categorical_accuracy: 0.3874 - val_loss: 0.1456 - val_categorical_accuracy: 0.4250
Epoch 341/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1470 - categorical_accuracy: 0.3874 - val_loss: 0.1453 - val_categorical_accuracy: 0.4250
Epoch 342/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1467 - categorical_accuracy: 0.3874 - val_loss: 0.1449 - val_categorical_accuracy: 0.4250
Epoch 343/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1463 - categorical_accuracy: 0.3874 - val_loss: 0.1445 - val_categorical_accuracy: 0.4250
Epoch 344/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1460 - categorical_accuracy: 0.3874 - val_loss: 0.1441 - val_categorical_accuracy: 0.4250
Epoch 345/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1456 - categorical_accuracy: 0.3874 - val_loss: 0.1438 - val_categorical_accuracy: 0.4250
Epoch 346/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1452 - categorical_accuracy: 0.3874 - val_loss: 0.1434 - val_categorical_accuracy: 0.4250
Epoch 347/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1448 - categorical_accuracy: 0.3874 - val_loss: 0.1431 - val_categorical_accuracy: 0.4250
Epoch 348/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1445 - categorical_accuracy: 0.3874 - val_loss: 0.1427 - val_categorical_accuracy: 0.4250
Epoch 349/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1442 - categorical_accuracy: 0.3874 - val_loss: 0.1423 - val_categorical_accuracy: 0.4250
Epoch 350/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1438 - categorical_accuracy: 0.3874 - val_loss: 0.1420 - val_categorical_accuracy: 0.4250
Epoch 351/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1434 - categorical_accuracy: 0.3874 - val_loss: 0.1416 - val_categorical_accuracy: 0.4250
Epoch 352/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1431 - categorical_accuracy: 0.3874 - val_loss: 0.1413 - val_categorical_accuracy: 0.4250
Epoch 353/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1427 - categorical_accuracy: 0.3874 - val_loss: 0.1409 - val_categorical_accuracy: 0.4250
Epoch 354/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1424 - categorical_accuracy: 0.3874 - val_loss: 0.1406 - val_categorical_accuracy: 0.4250
Epoch 355/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1420 - categorical_accuracy: 0.3874 - val_loss: 0.1402 - val_categorical_accuracy: 0.4250
Epoch 356/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1417 - categorical_accuracy: 0.3874 - val_loss: 0.1399 - val_categorical_accuracy: 0.4250
Epoch 357/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1413 - categorical_accuracy: 0.3874 - val_loss: 0.1395 - val_categorical_accuracy: 0.4250
Epoch 358/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1410 - categorical_accuracy: 0.3874 - val_loss: 0.1392 - val_categorical_accuracy: 0.4250
Epoch 359/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1406 - categorical_accuracy: 0.3874 - val_loss: 0.1388 - val_categorical_accuracy: 0.4250
Epoch 360/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1403 - categorical_accuracy: 0.3874 - val_loss: 0.1385 - val_categorical_accuracy: 0.4250
Epoch 361/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1400 - categorical_accuracy: 0.3874 - val_loss: 0.1382 - val_categorical_accuracy: 0.4250
Epoch 362/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1396 - categorical_accuracy: 0.3874 - val_loss: 0.1378 - val_categorical_accuracy: 0.4250
Epoch 363/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1393 - categorical_accuracy: 0.3874 - val_loss: 0.1375 - val_categorical_accuracy: 0.4250
Epoch 364/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1389 - categorical_accuracy: 0.3874 - val_loss: 0.1372 - val_categorical_accuracy: 0.4250
Epoch 365/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1386 - categorical_accuracy: 0.3874 - val_loss: 0.1368 - val_categorical_accuracy: 0.4250
Epoch 366/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1383 - categorical_accuracy: 0.3874 - val_loss: 0.1365 - val_categorical_accuracy: 0.4250
Epoch 367/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1380 - categorical_accuracy: 0.3874 - val_loss: 0.1362 - val_categorical_accuracy: 0.4250
Epoch 368/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1376 - categorical_accuracy: 0.3874 - val_loss: 0.1358 - val_categorical_accuracy: 0.4250
Epoch 369/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1373 - categorical_accuracy: 0.3874 - val_loss: 0.1355 - val_categorical_accuracy: 0.4250
Epoch 370/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1370 - categorical_accuracy: 0.3874 - val_loss: 0.1352 - val_categorical_accuracy: 0.4250
Epoch 371/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1367 - categorical_accuracy: 0.3874 - val_loss: 0.1348 - val_categorical_accuracy: 0.4250
Epoch 372/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1363 - categorical_accuracy: 0.3874 - val_loss: 0.1345 - val_categorical_accuracy: 0.4250
Epoch 373/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1360 - categorical_accuracy: 0.3874 - val_loss: 0.1342 - val_categorical_accuracy: 0.4250
Epoch 374/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1357 - categorical_accuracy: 0.3874 - val_loss: 0.1339 - val_categorical_accuracy: 0.4250
Epoch 375/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1353 - categorical_accuracy: 0.3874 - val_loss: 0.1336 - val_categorical_accuracy: 0.4250
Epoch 376/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1350 - categorical_accuracy: 0.3874 - val_loss: 0.1332 - val_categorical_accuracy: 0.4250
Epoch 377/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1347 - categorical_accuracy: 0.3874 - val_loss: 0.1329 - val_categorical_accuracy: 0.4250
Epoch 378/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1344 - categorical_accuracy: 0.3874 - val_loss: 0.1326 - val_categorical_accuracy: 0.4250
Epoch 379/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1341 - categorical_accuracy: 0.3874 - val_loss: 0.1323 - val_categorical_accuracy: 0.4250
Epoch 380/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1338 - categorical_accuracy: 0.3874 - val_loss: 0.1320 - val_categorical_accuracy: 0.4250
Epoch 381/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1334 - categorical_accuracy: 0.3874 - val_loss: 0.1317 - val_categorical_accuracy: 0.4250
Epoch 382/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1331 - categorical_accuracy: 0.3874 - val_loss: 0.1313 - val_categorical_accuracy: 0.4250
Epoch 383/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1328 - categorical_accuracy: 0.3874 - val_loss: 0.1310 - val_categorical_accuracy: 0.4250
Epoch 384/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1325 - categorical_accuracy: 0.3874 - val_loss: 0.1307 - val_categorical_accuracy: 0.4250
Epoch 385/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1322 - categorical_accuracy: 0.3874 - val_loss: 0.1304 - val_categorical_accuracy: 0.4250
Epoch 386/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1319 - categorical_accuracy: 0.3874 - val_loss: 0.1301 - val_categorical_accuracy: 0.4250
Epoch 387/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1316 - categorical_accuracy: 0.3874 - val_loss: 0.1298 - val_categorical_accuracy: 0.4250
Epoch 388/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1312 - categorical_accuracy: 0.3874 - val_loss: 0.1295 - val_categorical_accuracy: 0.4250
Epoch 389/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1310 - categorical_accuracy: 0.3874 - val_loss: 0.1292 - val_categorical_accuracy: 0.4250
Epoch 390/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1307 - categorical_accuracy: 0.3874 - val_loss: 0.1289 - val_categorical_accuracy: 0.4250
Epoch 391/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1304 - categorical_accuracy: 0.3874 - val_loss: 0.1286 - val_categorical_accuracy: 0.4250
Epoch 392/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1301 - categorical_accuracy: 0.3874 - val_loss: 0.1283 - val_categorical_accuracy: 0.4250
Epoch 393/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1298 - categorical_accuracy: 0.3874 - val_loss: 0.1280 - val_categorical_accuracy: 0.4250
Epoch 394/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1295 - categorical_accuracy: 0.3874 - val_loss: 0.1277 - val_categorical_accuracy: 0.4250
Epoch 395/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1292 - categorical_accuracy: 0.3874 - val_loss: 0.1274 - val_categorical_accuracy: 0.4250
Epoch 396/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1289 - categorical_accuracy: 0.3874 - val_loss: 0.1271 - val_categorical_accuracy: 0.4250
Epoch 397/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1286 - categorical_accuracy: 0.3874 - val_loss: 0.1268 - val_categorical_accuracy: 0.4250
Epoch 398/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1283 - categorical_accuracy: 0.3874 - val_loss: 0.1266 - val_categorical_accuracy: 0.4250
Epoch 399/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1280 - categorical_accuracy: 0.3874 - val_loss: 0.1263 - val_categorical_accuracy: 0.4250
Epoch 400/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1277 - categorical_accuracy: 0.3874 - val_loss: 0.1260 - val_categorical_accuracy: 0.4250
Epoch 401/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1274 - categorical_accuracy: 0.3874 - val_loss: 0.1257 - val_categorical_accuracy: 0.4250
Epoch 402/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1272 - categorical_accuracy: 0.3874 - val_loss: 0.1254 - val_categorical_accuracy: 0.4250
Epoch 403/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1269 - categorical_accuracy: 0.3874 - val_loss: 0.1251 - val_categorical_accuracy: 0.4250
Epoch 404/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1265 - categorical_accuracy: 0.3874 - val_loss: 0.1248 - val_categorical_accuracy: 0.4250
Epoch 405/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1263 - categorical_accuracy: 0.3874 - val_loss: 0.1246 - val_categorical_accuracy: 0.4250
Epoch 406/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1260 - categorical_accuracy: 0.3874 - val_loss: 0.1243 - val_categorical_accuracy: 0.4250
Epoch 407/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1257 - categorical_accuracy: 0.3874 - val_loss: 0.1240 - val_categorical_accuracy: 0.4250
Epoch 408/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1254 - categorical_accuracy: 0.3874 - val_loss: 0.1237 - val_categorical_accuracy: 0.4250
Epoch 409/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1251 - categorical_accuracy: 0.3874 - val_loss: 0.1234 - val_categorical_accuracy: 0.4250
Epoch 410/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1249 - categorical_accuracy: 0.3874 - val_loss: 0.1232 - val_categorical_accuracy: 0.4250
Epoch 411/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1246 - categorical_accuracy: 0.3874 - val_loss: 0.1229 - val_categorical_accuracy: 0.4250
Epoch 412/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1243 - categorical_accuracy: 0.3874 - val_loss: 0.1226 - val_categorical_accuracy: 0.4250
Epoch 413/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1241 - categorical_accuracy: 0.3874 - val_loss: 0.1224 - val_categorical_accuracy: 0.4250
Epoch 414/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1238 - categorical_accuracy: 0.3874 - val_loss: 0.1221 - val_categorical_accuracy: 0.4250
Epoch 415/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1235 - categorical_accuracy: 0.3874 - val_loss: 0.1218 - val_categorical_accuracy: 0.4250
Epoch 416/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1232 - categorical_accuracy: 0.3874 - val_loss: 0.1215 - val_categorical_accuracy: 0.4250
Epoch 417/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1230 - categorical_accuracy: 0.3874 - val_loss: 0.1213 - val_categorical_accuracy: 0.4250
Epoch 418/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1227 - categorical_accuracy: 0.3874 - val_loss: 0.1210 - val_categorical_accuracy: 0.4250
Epoch 419/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1224 - categorical_accuracy: 0.3874 - val_loss: 0.1208 - val_categorical_accuracy: 0.4250
Epoch 420/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1222 - categorical_accuracy: 0.3874 - val_loss: 0.1205 - val_categorical_accuracy: 0.4250
Epoch 421/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1219 - categorical_accuracy: 0.3874 - val_loss: 0.1202 - val_categorical_accuracy: 0.4250
Epoch 422/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1216 - categorical_accuracy: 0.3874 - val_loss: 0.1200 - val_categorical_accuracy: 0.4250
Epoch 423/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1214 - categorical_accuracy: 0.3874 - val_loss: 0.1197 - val_categorical_accuracy: 0.4250
Epoch 424/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1211 - categorical_accuracy: 0.3874 - val_loss: 0.1195 - val_categorical_accuracy: 0.4250
Epoch 425/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1209 - categorical_accuracy: 0.3874 - val_loss: 0.1192 - val_categorical_accuracy: 0.4250
Epoch 426/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1206 - categorical_accuracy: 0.3874 - val_loss: 0.1189 - val_categorical_accuracy: 0.4250
Epoch 427/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1203 - categorical_accuracy: 0.3874 - val_loss: 0.1187 - val_categorical_accuracy: 0.4250
Epoch 428/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1201 - categorical_accuracy: 0.3874 - val_loss: 0.1184 - val_categorical_accuracy: 0.4250
Epoch 429/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1198 - categorical_accuracy: 0.3874 - val_loss: 0.1182 - val_categorical_accuracy: 0.4250
Epoch 430/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1196 - categorical_accuracy: 0.3874 - val_loss: 0.1179 - val_categorical_accuracy: 0.4250
Epoch 431/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1193 - categorical_accuracy: 0.3874 - val_loss: 0.1177 - val_categorical_accuracy: 0.4250
Epoch 432/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1191 - categorical_accuracy: 0.3874 - val_loss: 0.1174 - val_categorical_accuracy: 0.4250
Epoch 433/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1188 - categorical_accuracy: 0.3874 - val_loss: 0.1172 - val_categorical_accuracy: 0.4250
Epoch 434/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1186 - categorical_accuracy: 0.3874 - val_loss: 0.1169 - val_categorical_accuracy: 0.4250
Epoch 435/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1183 - categorical_accuracy: 0.3874 - val_loss: 0.1167 - val_categorical_accuracy: 0.4250
Epoch 436/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1181 - categorical_accuracy: 0.3874 - val_loss: 0.1165 - val_categorical_accuracy: 0.4250
Epoch 437/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1179 - categorical_accuracy: 0.3874 - val_loss: 0.1162 - val_categorical_accuracy: 0.4250
Epoch 438/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1176 - categorical_accuracy: 0.3874 - val_loss: 0.1160 - val_categorical_accuracy: 0.4250
Epoch 439/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1174 - categorical_accuracy: 0.3874 - val_loss: 0.1157 - val_categorical_accuracy: 0.4250
Epoch 440/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1171 - categorical_accuracy: 0.3874 - val_loss: 0.1155 - val_categorical_accuracy: 0.4250
Epoch 441/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1169 - categorical_accuracy: 0.3874 - val_loss: 0.1153 - val_categorical_accuracy: 0.4250
Epoch 442/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1166 - categorical_accuracy: 0.3874 - val_loss: 0.1150 - val_categorical_accuracy: 0.4250
Epoch 443/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1164 - categorical_accuracy: 0.3874 - val_loss: 0.1148 - val_categorical_accuracy: 0.4250
Epoch 444/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1161 - categorical_accuracy: 0.3874 - val_loss: 0.1145 - val_categorical_accuracy: 0.4250
Epoch 445/500
9/9 [==============================] - 0s 2ms/step - loss: 0.1159 - categorical_accuracy: 0.3874 - val_loss: 0.1143 - val_categorical_accuracy: 0.4250
Epoch 446/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1157 - categorical_accuracy: 0.3874 - val_loss: 0.1141 - val_categorical_accuracy: 0.4250
Epoch 447/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1154 - categorical_accuracy: 0.3874 - val_loss: 0.1139 - val_categorical_accuracy: 0.4250
Epoch 448/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1152 - categorical_accuracy: 0.3874 - val_loss: 0.1136 - val_categorical_accuracy: 0.4250
Epoch 449/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1150 - categorical_accuracy: 0.3874 - val_loss: 0.1134 - val_categorical_accuracy: 0.4250
Epoch 450/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1147 - categorical_accuracy: 0.3874 - val_loss: 0.1132 - val_categorical_accuracy: 0.4250
Epoch 451/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1145 - categorical_accuracy: 0.3874 - val_loss: 0.1129 - val_categorical_accuracy: 0.4250
Epoch 452/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1143 - categorical_accuracy: 0.3874 - val_loss: 0.1127 - val_categorical_accuracy: 0.4250
Epoch 453/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1141 - categorical_accuracy: 0.3874 - val_loss: 0.1125 - val_categorical_accuracy: 0.4250
Epoch 454/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1138 - categorical_accuracy: 0.3874 - val_loss: 0.1123 - val_categorical_accuracy: 0.4250
Epoch 455/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1136 - categorical_accuracy: 0.3874 - val_loss: 0.1120 - val_categorical_accuracy: 0.4250
Epoch 456/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1134 - categorical_accuracy: 0.3874 - val_loss: 0.1118 - val_categorical_accuracy: 0.4250
Epoch 457/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1132 - categorical_accuracy: 0.3874 - val_loss: 0.1116 - val_categorical_accuracy: 0.4250
Epoch 458/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1129 - categorical_accuracy: 0.3874 - val_loss: 0.1114 - val_categorical_accuracy: 0.4250
Epoch 459/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1127 - categorical_accuracy: 0.3874 - val_loss: 0.1112 - val_categorical_accuracy: 0.4250
Epoch 460/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1125 - categorical_accuracy: 0.3874 - val_loss: 0.1110 - val_categorical_accuracy: 0.4250
Epoch 461/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1123 - categorical_accuracy: 0.3874 - val_loss: 0.1107 - val_categorical_accuracy: 0.4250
Epoch 462/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1121 - categorical_accuracy: 0.3874 - val_loss: 0.1105 - val_categorical_accuracy: 0.4250
Epoch 463/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1119 - categorical_accuracy: 0.3874 - val_loss: 0.1103 - val_categorical_accuracy: 0.4250
Epoch 464/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1116 - categorical_accuracy: 0.3874 - val_loss: 0.1101 - val_categorical_accuracy: 0.4250
Epoch 465/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1114 - categorical_accuracy: 0.3874 - val_loss: 0.1099 - val_categorical_accuracy: 0.4250
Epoch 466/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1112 - categorical_accuracy: 0.3874 - val_loss: 0.1097 - val_categorical_accuracy: 0.4250
Epoch 467/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1110 - categorical_accuracy: 0.3874 - val_loss: 0.1095 - val_categorical_accuracy: 0.4250
Epoch 468/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - categorical_accuracy: 0.3874 - val_loss: 0.1093 - val_categorical_accuracy: 0.4250
Epoch 469/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1106 - categorical_accuracy: 0.3874 - val_loss: 0.1091 - val_categorical_accuracy: 0.4250
Epoch 470/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1104 - categorical_accuracy: 0.3874 - val_loss: 0.1089 - val_categorical_accuracy: 0.4250
Epoch 471/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1102 - categorical_accuracy: 0.3874 - val_loss: 0.1087 - val_categorical_accuracy: 0.4250
Epoch 472/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1100 - categorical_accuracy: 0.3874 - val_loss: 0.1085 - val_categorical_accuracy: 0.4250
Epoch 473/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1098 - categorical_accuracy: 0.3874 - val_loss: 0.1083 - val_categorical_accuracy: 0.4250
Epoch 474/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1095 - categorical_accuracy: 0.3874 - val_loss: 0.1081 - val_categorical_accuracy: 0.4250
Epoch 475/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1094 - categorical_accuracy: 0.3874 - val_loss: 0.1079 - val_categorical_accuracy: 0.4250
Epoch 476/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1092 - categorical_accuracy: 0.3874 - val_loss: 0.1077 - val_categorical_accuracy: 0.4250
Epoch 477/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1090 - categorical_accuracy: 0.3874 - val_loss: 0.1075 - val_categorical_accuracy: 0.4250
Epoch 478/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1088 - categorical_accuracy: 0.3874 - val_loss: 0.1073 - val_categorical_accuracy: 0.4250
Epoch 479/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1086 - categorical_accuracy: 0.3874 - val_loss: 0.1071 - val_categorical_accuracy: 0.4250
Epoch 480/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1084 - categorical_accuracy: 0.3874 - val_loss: 0.1069 - val_categorical_accuracy: 0.4250
Epoch 481/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1082 - categorical_accuracy: 0.3874 - val_loss: 0.1067 - val_categorical_accuracy: 0.4250
Epoch 482/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1080 - categorical_accuracy: 0.3874 - val_loss: 0.1065 - val_categorical_accuracy: 0.4250
Epoch 483/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1078 - categorical_accuracy: 0.3874 - val_loss: 0.1063 - val_categorical_accuracy: 0.4250
Epoch 484/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1076 - categorical_accuracy: 0.3874 - val_loss: 0.1061 - val_categorical_accuracy: 0.4250
Epoch 485/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1074 - categorical_accuracy: 0.3874 - val_loss: 0.1059 - val_categorical_accuracy: 0.4250
Epoch 486/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1072 - categorical_accuracy: 0.3874 - val_loss: 0.1057 - val_categorical_accuracy: 0.4250
Epoch 487/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1070 - categorical_accuracy: 0.3874 - val_loss: 0.1056 - val_categorical_accuracy: 0.4250
Epoch 488/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1069 - categorical_accuracy: 0.3874 - val_loss: 0.1054 - val_categorical_accuracy: 0.4250
Epoch 489/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1067 - categorical_accuracy: 0.3874 - val_loss: 0.1052 - val_categorical_accuracy: 0.4250
Epoch 490/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1065 - categorical_accuracy: 0.3874 - val_loss: 0.1050 - val_categorical_accuracy: 0.4250
Epoch 491/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1063 - categorical_accuracy: 0.3874 - val_loss: 0.1048 - val_categorical_accuracy: 0.4250
Epoch 492/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1061 - categorical_accuracy: 0.3874 - val_loss: 0.1046 - val_categorical_accuracy: 0.4250
Epoch 493/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1059 - categorical_accuracy: 0.3874 - val_loss: 0.1045 - val_categorical_accuracy: 0.4250
Epoch 494/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1058 - categorical_accuracy: 0.3874 - val_loss: 0.1043 - val_categorical_accuracy: 0.4250
Epoch 495/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1056 - categorical_accuracy: 0.3874 - val_loss: 0.1041 - val_categorical_accuracy: 0.4250
Epoch 496/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1054 - categorical_accuracy: 0.3874 - val_loss: 0.1039 - val_categorical_accuracy: 0.4250
Epoch 497/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1052 - categorical_accuracy: 0.3874 - val_loss: 0.1038 - val_categorical_accuracy: 0.4250
Epoch 498/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1050 - categorical_accuracy: 0.3874 - val_loss: 0.1036 - val_categorical_accuracy: 0.4250
Epoch 499/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1049 - categorical_accuracy: 0.3874 - val_loss: 0.1034 - val_categorical_accuracy: 0.4250
Epoch 500/500
9/9 [==============================] - 0s 3ms/step - loss: 0.1047 - categorical_accuracy: 0.3874 - val_loss: 0.1032 - val_categorical_accuracy: 0.4250
Total Time Taken is : -16.27851152420044
y_pred_cat_3=model_cat_3.predict(X_test)
print("The Accuracy of the model is : ",accuracy_score(y_test,convert_to_class_labels(y_pred_cat_3)))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_test,convert_to_class_labels(y_pred_cat_3)),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.3972222222222222
history=history_cat_3.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["categorical_accuracy"])
ax.set_title("Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
#
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["val_loss"])
ax.set_title("Validation loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["val_categorical_accuracy"])
ax.set_title("Validation Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'categorical_accuracy', 'val_loss', 'val_categorical_accuracy'])
###################################################################
#Regressional Neural Network
###################################################################
model_reg_3=k.Sequential()
model_reg_3.add(BatchNormalization(input_shape=(X_train1.shape[1],)))
model_reg_3.add(Flatten())
model_reg_3.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dropout(0.2, input_shape=(50,)))
model_reg_3.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dropout(0.2, input_shape=(50,)))
model_reg_3.add(Dense(30,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dropout(0.5, input_shape=(50,)))
model_reg_3.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dropout(0.2, input_shape=(50,)))
model_reg_3.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dropout(0.5, input_shape=(30,)))
model_reg_3.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_3.add(Dense(1))
sgd = optimizers.SGD(lr = 0.01,momentum=0.6)
model_reg_3.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.MeanSquaredError())
###################################################################
#
###################################################################
t=time.time()
history_reg_3=model_reg_3.fit(X_train,y_train,validation_data = (X_valid.to_numpy(),y_valid),batch_size=100, epochs = 500) #add verbose later
print("Total Time Taken is : ",t-time.time())
Epoch 1/500 1/9 [==>...........................] - ETA: 0s - loss: 32.2499 - mean_squared_error: 32.1600WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_test_batch_end` time: 0.0010s). Check your callbacks. 9/9 [==============================] - 0s 28ms/step - loss: 19.2670 - mean_squared_error: 19.1709 - val_loss: 1.6159 - val_mean_squared_error: 1.4973 Epoch 2/500 9/9 [==============================] - 0s 3ms/step - loss: 0.9656 - mean_squared_error: 0.8386 - val_loss: 0.7380 - val_mean_squared_error: 0.6117 Epoch 3/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7800 - mean_squared_error: 0.6544 - val_loss: 0.7377 - val_mean_squared_error: 0.6124 Epoch 4/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7779 - mean_squared_error: 0.6532 - val_loss: 0.7360 - val_mean_squared_error: 0.6117 Epoch 5/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7758 - mean_squared_error: 0.6519 - val_loss: 0.7345 - val_mean_squared_error: 0.6112 Epoch 6/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7803 - mean_squared_error: 0.6574 - val_loss: 0.7338 - val_mean_squared_error: 0.6114 Epoch 7/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7710 - mean_squared_error: 0.6489 - val_loss: 0.7335 - val_mean_squared_error: 0.6126 Epoch 8/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7721 - mean_squared_error: 0.6513 - val_loss: 0.7315 - val_mean_squared_error: 0.6112 Epoch 9/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7807 - mean_squared_error: 0.6607 - val_loss: 0.7334 - val_mean_squared_error: 0.6135 Epoch 10/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7768 - mean_squared_error: 0.6577 - val_loss: 0.7315 - val_mean_squared_error: 0.6126 Epoch 11/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7758 - mean_squared_error: 0.6575 - val_loss: 0.7295 - val_mean_squared_error: 0.6117 Epoch 12/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7664 - mean_squared_error: 0.6490 - val_loss: 0.7283 - val_mean_squared_error: 0.6118 Epoch 13/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7699 - mean_squared_error: 0.6534 - val_loss: 0.7284 - val_mean_squared_error: 0.6129 Epoch 14/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7727 - mean_squared_error: 0.6573 - val_loss: 0.7261 - val_mean_squared_error: 0.6112 Epoch 15/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7669 - mean_squared_error: 0.6524 - val_loss: 0.7257 - val_mean_squared_error: 0.6115 Epoch 16/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7663 - mean_squared_error: 0.6524 - val_loss: 0.7246 - val_mean_squared_error: 0.6116 Epoch 17/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7671 - mean_squared_error: 0.6544 - val_loss: 0.7239 - val_mean_squared_error: 0.6114 Epoch 18/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7642 - mean_squared_error: 0.6522 - val_loss: 0.7238 - val_mean_squared_error: 0.6121 Epoch 19/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7597 - mean_squared_error: 0.6483 - val_loss: 0.7228 - val_mean_squared_error: 0.6125 Epoch 20/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7594 - mean_squared_error: 0.6494 - val_loss: 0.7286 - val_mean_squared_error: 0.6181 Epoch 21/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7649 - mean_squared_error: 0.6550 - val_loss: 0.7202 - val_mean_squared_error: 0.6114 Epoch 22/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7610 - mean_squared_error: 0.6524 - val_loss: 0.7197 - val_mean_squared_error: 0.6115 Epoch 23/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7597 - mean_squared_error: 0.6517 - val_loss: 0.7185 - val_mean_squared_error: 0.6113 Epoch 24/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7616 - mean_squared_error: 0.6547 - val_loss: 0.7183 - val_mean_squared_error: 0.6116 Epoch 25/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7570 - mean_squared_error: 0.6507 - val_loss: 0.7169 - val_mean_squared_error: 0.6112 Epoch 26/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7547 - mean_squared_error: 0.6493 - val_loss: 0.7171 - val_mean_squared_error: 0.6119 Epoch 27/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7573 - mean_squared_error: 0.6524 - val_loss: 0.7161 - val_mean_squared_error: 0.6123 Epoch 28/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7537 - mean_squared_error: 0.6500 - val_loss: 0.7147 - val_mean_squared_error: 0.6115 Epoch 29/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7543 - mean_squared_error: 0.6514 - val_loss: 0.7166 - val_mean_squared_error: 0.6136 Epoch 30/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7558 - mean_squared_error: 0.6532 - val_loss: 0.7130 - val_mean_squared_error: 0.6113 Epoch 31/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7511 - mean_squared_error: 0.6497 - val_loss: 0.7147 - val_mean_squared_error: 0.6133 Epoch 32/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7508 - mean_squared_error: 0.6500 - val_loss: 0.7116 - val_mean_squared_error: 0.6113 Epoch 33/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7503 - mean_squared_error: 0.6504 - val_loss: 0.7109 - val_mean_squared_error: 0.6113 Epoch 34/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7553 - mean_squared_error: 0.6560 - val_loss: 0.7107 - val_mean_squared_error: 0.6122 Epoch 35/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7522 - mean_squared_error: 0.6538 - val_loss: 0.7093 - val_mean_squared_error: 0.6112 Epoch 36/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7498 - mean_squared_error: 0.6518 - val_loss: 0.7092 - val_mean_squared_error: 0.6117 Epoch 37/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7472 - mean_squared_error: 0.6500 - val_loss: 0.7093 - val_mean_squared_error: 0.6131 Epoch 38/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7468 - mean_squared_error: 0.6507 - val_loss: 0.7082 - val_mean_squared_error: 0.6121 Epoch 39/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7502 - mean_squared_error: 0.6545 - val_loss: 0.7079 - val_mean_squared_error: 0.6125 Epoch 40/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7492 - mean_squared_error: 0.6539 - val_loss: 0.7064 - val_mean_squared_error: 0.6122 Epoch 41/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7480 - mean_squared_error: 0.6539 - val_loss: 0.7058 - val_mean_squared_error: 0.6118 Epoch 42/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7469 - mean_squared_error: 0.6534 - val_loss: 0.7061 - val_mean_squared_error: 0.6127 Epoch 43/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7440 - mean_squared_error: 0.6509 - val_loss: 0.7044 - val_mean_squared_error: 0.6124 Epoch 44/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7448 - mean_squared_error: 0.6530 - val_loss: 0.7058 - val_mean_squared_error: 0.6136 Epoch 45/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7443 - mean_squared_error: 0.6527 - val_loss: 0.7024 - val_mean_squared_error: 0.6113 Epoch 46/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7433 - mean_squared_error: 0.6523 - val_loss: 0.7016 - val_mean_squared_error: 0.6112 Epoch 47/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7444 - mean_squared_error: 0.6540 - val_loss: 0.7009 - val_mean_squared_error: 0.6112 Epoch 48/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7390 - mean_squared_error: 0.6494 - val_loss: 0.7003 - val_mean_squared_error: 0.6112 Epoch 49/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7393 - mean_squared_error: 0.6505 - val_loss: 0.6998 - val_mean_squared_error: 0.6114 Epoch 50/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7387 - mean_squared_error: 0.6504 - val_loss: 0.6989 - val_mean_squared_error: 0.6113 Epoch 51/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7389 - mean_squared_error: 0.6515 - val_loss: 0.6983 - val_mean_squared_error: 0.6112 Epoch 52/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7400 - mean_squared_error: 0.6532 - val_loss: 0.6983 - val_mean_squared_error: 0.6117 Epoch 53/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7366 - mean_squared_error: 0.6504 - val_loss: 0.6972 - val_mean_squared_error: 0.6113 Epoch 54/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7375 - mean_squared_error: 0.6519 - val_loss: 0.6965 - val_mean_squared_error: 0.6112 Epoch 55/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7349 - mean_squared_error: 0.6499 - val_loss: 0.6958 - val_mean_squared_error: 0.6112 Epoch 56/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7345 - mean_squared_error: 0.6504 - val_loss: 0.7005 - val_mean_squared_error: 0.6160 Epoch 57/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7375 - mean_squared_error: 0.6534 - val_loss: 0.6946 - val_mean_squared_error: 0.6114 Epoch 58/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7362 - mean_squared_error: 0.6532 - val_loss: 0.6939 - val_mean_squared_error: 0.6112 Epoch 59/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7339 - mean_squared_error: 0.6514 - val_loss: 0.6942 - val_mean_squared_error: 0.6119 Epoch 60/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7340 - mean_squared_error: 0.6521 - val_loss: 0.6957 - val_mean_squared_error: 0.6137 Epoch 61/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7361 - mean_squared_error: 0.6543 - val_loss: 0.6922 - val_mean_squared_error: 0.6112 Epoch 62/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7315 - mean_squared_error: 0.6506 - val_loss: 0.6921 - val_mean_squared_error: 0.6116 Epoch 63/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7306 - mean_squared_error: 0.6502 - val_loss: 0.6913 - val_mean_squared_error: 0.6116 Epoch 64/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7305 - mean_squared_error: 0.6511 - val_loss: 0.6915 - val_mean_squared_error: 0.6120 Epoch 65/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7297 - mean_squared_error: 0.6505 - val_loss: 0.6901 - val_mean_squared_error: 0.6116 Epoch 66/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7305 - mean_squared_error: 0.6521 - val_loss: 0.6893 - val_mean_squared_error: 0.6112 Epoch 67/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7306 - mean_squared_error: 0.6529 - val_loss: 0.6994 - val_mean_squared_error: 0.6210 Epoch 68/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7333 - mean_squared_error: 0.6555 - val_loss: 0.6889 - val_mean_squared_error: 0.6117 Epoch 69/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7297 - mean_squared_error: 0.6527 - val_loss: 0.6889 - val_mean_squared_error: 0.6122 Epoch 70/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7285 - mean_squared_error: 0.6521 - val_loss: 0.6871 - val_mean_squared_error: 0.6112 Epoch 71/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7251 - mean_squared_error: 0.6494 - val_loss: 0.6892 - val_mean_squared_error: 0.6134 Epoch 72/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7290 - mean_squared_error: 0.6535 - val_loss: 0.6861 - val_mean_squared_error: 0.6114 Epoch 73/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7266 - mean_squared_error: 0.6519 - val_loss: 0.6856 - val_mean_squared_error: 0.6112 Epoch 74/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7249 - mean_squared_error: 0.6506 - val_loss: 0.6850 - val_mean_squared_error: 0.6113 Epoch 75/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7249 - mean_squared_error: 0.6516 - val_loss: 0.6890 - val_mean_squared_error: 0.6152 Epoch 76/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7242 - mean_squared_error: 0.6509 - val_loss: 0.6845 - val_mean_squared_error: 0.6116 Epoch 77/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7250 - mean_squared_error: 0.6522 - val_loss: 0.6869 - val_mean_squared_error: 0.6152 Epoch 78/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7282 - mean_squared_error: 0.6563 - val_loss: 0.6842 - val_mean_squared_error: 0.6128 Epoch 79/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7249 - mean_squared_error: 0.6533 - val_loss: 0.6825 - val_mean_squared_error: 0.6113 Epoch 80/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7219 - mean_squared_error: 0.6508 - val_loss: 0.6819 - val_mean_squared_error: 0.6112 Epoch 81/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7213 - mean_squared_error: 0.6507 - val_loss: 0.6824 - val_mean_squared_error: 0.6124 Epoch 82/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7226 - mean_squared_error: 0.6528 - val_loss: 0.6819 - val_mean_squared_error: 0.6119 Epoch 83/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7231 - mean_squared_error: 0.6531 - val_loss: 0.6829 - val_mean_squared_error: 0.6141 Epoch 84/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7211 - mean_squared_error: 0.6522 - val_loss: 0.6811 - val_mean_squared_error: 0.6121 Epoch 85/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7239 - mean_squared_error: 0.6552 - val_loss: 0.6799 - val_mean_squared_error: 0.6114 Epoch 86/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7185 - mean_squared_error: 0.6501 - val_loss: 0.6822 - val_mean_squared_error: 0.6149 Epoch 87/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7237 - mean_squared_error: 0.6563 - val_loss: 0.6786 - val_mean_squared_error: 0.6112 Epoch 88/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7176 - mean_squared_error: 0.6504 - val_loss: 0.6790 - val_mean_squared_error: 0.6119 Epoch 89/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7175 - mean_squared_error: 0.6506 - val_loss: 0.6777 - val_mean_squared_error: 0.6113 Epoch 90/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7177 - mean_squared_error: 0.6515 - val_loss: 0.6775 - val_mean_squared_error: 0.6114 Epoch 91/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7189 - mean_squared_error: 0.6530 - val_loss: 0.6769 - val_mean_squared_error: 0.6115 Epoch 92/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7189 - mean_squared_error: 0.6534 - val_loss: 0.6768 - val_mean_squared_error: 0.6119 Epoch 93/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7185 - mean_squared_error: 0.6538 - val_loss: 0.6809 - val_mean_squared_error: 0.6157 Epoch 94/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7204 - mean_squared_error: 0.6554 - val_loss: 0.6756 - val_mean_squared_error: 0.6113 Epoch 95/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7154 - mean_squared_error: 0.6512 - val_loss: 0.6750 - val_mean_squared_error: 0.6112 Epoch 96/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7137 - mean_squared_error: 0.6501 - val_loss: 0.6758 - val_mean_squared_error: 0.6122 Epoch 97/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7133 - mean_squared_error: 0.6500 - val_loss: 0.6741 - val_mean_squared_error: 0.6112 Epoch 98/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7143 - mean_squared_error: 0.6515 - val_loss: 0.6736 - val_mean_squared_error: 0.6112 Epoch 99/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7137 - mean_squared_error: 0.6513 - val_loss: 0.6733 - val_mean_squared_error: 0.6112 Epoch 100/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7137 - mean_squared_error: 0.6518 - val_loss: 0.6731 - val_mean_squared_error: 0.6114 Epoch 101/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7121 - mean_squared_error: 0.6506 - val_loss: 0.6724 - val_mean_squared_error: 0.6112 Epoch 102/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7115 - mean_squared_error: 0.6504 - val_loss: 0.6720 - val_mean_squared_error: 0.6112 Epoch 103/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7117 - mean_squared_error: 0.6510 - val_loss: 0.6716 - val_mean_squared_error: 0.6112 Epoch 104/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7109 - mean_squared_error: 0.6506 - val_loss: 0.6712 - val_mean_squared_error: 0.6112 Epoch 105/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7102 - mean_squared_error: 0.6505 - val_loss: 0.6728 - val_mean_squared_error: 0.6129 Epoch 106/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7112 - mean_squared_error: 0.6514 - val_loss: 0.6707 - val_mean_squared_error: 0.6117 Epoch 107/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7107 - mean_squared_error: 0.6518 - val_loss: 0.6723 - val_mean_squared_error: 0.6131 Epoch 108/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7121 - mean_squared_error: 0.6532 - val_loss: 0.6696 - val_mean_squared_error: 0.6113 Epoch 109/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7134 - mean_squared_error: 0.6547 - val_loss: 0.6714 - val_mean_squared_error: 0.6139 Epoch 110/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7130 - mean_squared_error: 0.6551 - val_loss: 0.6688 - val_mean_squared_error: 0.6113 Epoch 111/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7086 - mean_squared_error: 0.6513 - val_loss: 0.6709 - val_mean_squared_error: 0.6133 Epoch 112/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7078 - mean_squared_error: 0.6504 - val_loss: 0.6681 - val_mean_squared_error: 0.6113 Epoch 113/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7094 - mean_squared_error: 0.6526 - val_loss: 0.6677 - val_mean_squared_error: 0.6112 Epoch 114/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7072 - mean_squared_error: 0.6508 - val_loss: 0.6673 - val_mean_squared_error: 0.6114 Epoch 115/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7096 - mean_squared_error: 0.6534 - val_loss: 0.6673 - val_mean_squared_error: 0.6118 Epoch 116/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7071 - mean_squared_error: 0.6514 - val_loss: 0.6666 - val_mean_squared_error: 0.6114 Epoch 117/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7079 - mean_squared_error: 0.6529 - val_loss: 0.6694 - val_mean_squared_error: 0.6139 Epoch 118/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7053 - mean_squared_error: 0.6501 - val_loss: 0.6663 - val_mean_squared_error: 0.6119 Epoch 119/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7058 - mean_squared_error: 0.6513 - val_loss: 0.6655 - val_mean_squared_error: 0.6112 Epoch 120/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7054 - mean_squared_error: 0.6514 - val_loss: 0.6691 - val_mean_squared_error: 0.6147 Epoch 121/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7079 - mean_squared_error: 0.6538 - val_loss: 0.6647 - val_mean_squared_error: 0.6112 Epoch 122/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7053 - mean_squared_error: 0.6520 - val_loss: 0.6649 - val_mean_squared_error: 0.6119 Epoch 123/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7041 - mean_squared_error: 0.6512 - val_loss: 0.6653 - val_mean_squared_error: 0.6122 Epoch 124/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7040 - mean_squared_error: 0.6512 - val_loss: 0.6642 - val_mean_squared_error: 0.6115 Epoch 125/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7031 - mean_squared_error: 0.6507 - val_loss: 0.6658 - val_mean_squared_error: 0.6132 Epoch 126/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7016 - mean_squared_error: 0.6492 - val_loss: 0.6641 - val_mean_squared_error: 0.6125 Epoch 127/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7056 - mean_squared_error: 0.6541 - val_loss: 0.6633 - val_mean_squared_error: 0.6117 Epoch 128/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7034 - mean_squared_error: 0.6519 - val_loss: 0.6639 - val_mean_squared_error: 0.6124 Epoch 129/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7043 - mean_squared_error: 0.6531 - val_loss: 0.6639 - val_mean_squared_error: 0.6127 Epoch 130/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7018 - mean_squared_error: 0.6508 - val_loss: 0.6617 - val_mean_squared_error: 0.6112 Epoch 131/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7037 - mean_squared_error: 0.6532 - val_loss: 0.6623 - val_mean_squared_error: 0.6119 Epoch 132/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7006 - mean_squared_error: 0.6504 - val_loss: 0.6618 - val_mean_squared_error: 0.6117 Epoch 133/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7037 - mean_squared_error: 0.6539 - val_loss: 0.6613 - val_mean_squared_error: 0.6115 Epoch 134/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7008 - mean_squared_error: 0.6515 - val_loss: 0.6651 - val_mean_squared_error: 0.6153 Epoch 135/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7005 - mean_squared_error: 0.6510 - val_loss: 0.6610 - val_mean_squared_error: 0.6124 Epoch 136/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7015 - mean_squared_error: 0.6529 - val_loss: 0.6602 - val_mean_squared_error: 0.6115 Epoch 137/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6987 - mean_squared_error: 0.6500 - val_loss: 0.6607 - val_mean_squared_error: 0.6121 Epoch 138/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6988 - mean_squared_error: 0.6502 - val_loss: 0.6616 - val_mean_squared_error: 0.6140 Epoch 139/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7017 - mean_squared_error: 0.6541 - val_loss: 0.6620 - val_mean_squared_error: 0.6138 Epoch 140/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6990 - mean_squared_error: 0.6512 - val_loss: 0.6590 - val_mean_squared_error: 0.6118 Epoch 141/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6983 - mean_squared_error: 0.6512 - val_loss: 0.6613 - val_mean_squared_error: 0.6137 Epoch 142/500 9/9 [==============================] - 0s 2ms/step - loss: 0.7002 - mean_squared_error: 0.6529 - val_loss: 0.6580 - val_mean_squared_error: 0.6112 Epoch 143/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6971 - mean_squared_error: 0.6504 - val_loss: 0.6598 - val_mean_squared_error: 0.6130 Epoch 144/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6983 - mean_squared_error: 0.6519 - val_loss: 0.6598 - val_mean_squared_error: 0.6132 Epoch 145/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6962 - mean_squared_error: 0.6497 - val_loss: 0.6600 - val_mean_squared_error: 0.6145 Epoch 146/500 9/9 [==============================] - 0s 3ms/step - loss: 0.7004 - mean_squared_error: 0.6547 - val_loss: 0.6569 - val_mean_squared_error: 0.6112 Epoch 147/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6958 - mean_squared_error: 0.6503 - val_loss: 0.6568 - val_mean_squared_error: 0.6113 Epoch 148/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6962 - mean_squared_error: 0.6509 - val_loss: 0.6662 - val_mean_squared_error: 0.6204 Epoch 149/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6980 - mean_squared_error: 0.6526 - val_loss: 0.6560 - val_mean_squared_error: 0.6112 Epoch 150/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6942 - mean_squared_error: 0.6494 - val_loss: 0.6566 - val_mean_squared_error: 0.6119 Epoch 151/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6954 - mean_squared_error: 0.6508 - val_loss: 0.6565 - val_mean_squared_error: 0.6120 Epoch 152/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6949 - mean_squared_error: 0.6505 - val_loss: 0.6553 - val_mean_squared_error: 0.6113 Epoch 153/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6952 - mean_squared_error: 0.6510 - val_loss: 0.6560 - val_mean_squared_error: 0.6125 Epoch 154/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6970 - mean_squared_error: 0.6534 - val_loss: 0.6554 - val_mean_squared_error: 0.6117 Epoch 155/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6946 - mean_squared_error: 0.6511 - val_loss: 0.6576 - val_mean_squared_error: 0.6140 Epoch 156/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6950 - mean_squared_error: 0.6515 - val_loss: 0.6544 - val_mean_squared_error: 0.6116 Epoch 157/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6943 - mean_squared_error: 0.6516 - val_loss: 0.6572 - val_mean_squared_error: 0.6141 Epoch 158/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6932 - mean_squared_error: 0.6503 - val_loss: 0.6536 - val_mean_squared_error: 0.6112 Epoch 159/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6927 - mean_squared_error: 0.6503 - val_loss: 0.6537 - val_mean_squared_error: 0.6117 Epoch 160/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6936 - mean_squared_error: 0.6515 - val_loss: 0.6531 - val_mean_squared_error: 0.6112 Epoch 161/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6930 - mean_squared_error: 0.6512 - val_loss: 0.6550 - val_mean_squared_error: 0.6130 Epoch 162/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6914 - mean_squared_error: 0.6496 - val_loss: 0.6526 - val_mean_squared_error: 0.6112 Epoch 163/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6909 - mean_squared_error: 0.6496 - val_loss: 0.6525 - val_mean_squared_error: 0.6113 Epoch 164/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6913 - mean_squared_error: 0.6501 - val_loss: 0.6522 - val_mean_squared_error: 0.6112 Epoch 165/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6913 - mean_squared_error: 0.6505 - val_loss: 0.6522 - val_mean_squared_error: 0.6114 Epoch 166/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6913 - mean_squared_error: 0.6506 - val_loss: 0.6517 - val_mean_squared_error: 0.6112 Epoch 167/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6900 - mean_squared_error: 0.6495 - val_loss: 0.6516 - val_mean_squared_error: 0.6116 Epoch 168/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6907 - mean_squared_error: 0.6505 - val_loss: 0.6518 - val_mean_squared_error: 0.6120 Epoch 169/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6932 - mean_squared_error: 0.6534 - val_loss: 0.6509 - val_mean_squared_error: 0.6113 Epoch 170/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6916 - mean_squared_error: 0.6519 - val_loss: 0.6517 - val_mean_squared_error: 0.6125 Epoch 171/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6905 - mean_squared_error: 0.6512 - val_loss: 0.6521 - val_mean_squared_error: 0.6126 Epoch 172/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6897 - mean_squared_error: 0.6503 - val_loss: 0.6518 - val_mean_squared_error: 0.6131 Epoch 173/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6901 - mean_squared_error: 0.6514 - val_loss: 0.6508 - val_mean_squared_error: 0.6119 Epoch 174/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6881 - mean_squared_error: 0.6492 - val_loss: 0.6499 - val_mean_squared_error: 0.6115 Epoch 175/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6899 - mean_squared_error: 0.6514 - val_loss: 0.6496 - val_mean_squared_error: 0.6112 Epoch 176/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6893 - mean_squared_error: 0.6508 - val_loss: 0.6508 - val_mean_squared_error: 0.6130 Epoch 177/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6887 - mean_squared_error: 0.6510 - val_loss: 0.6542 - val_mean_squared_error: 0.6158 Epoch 178/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6902 - mean_squared_error: 0.6520 - val_loss: 0.6489 - val_mean_squared_error: 0.6113 Epoch 179/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6900 - mean_squared_error: 0.6523 - val_loss: 0.6492 - val_mean_squared_error: 0.6120 Epoch 180/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6891 - mean_squared_error: 0.6517 - val_loss: 0.6485 - val_mean_squared_error: 0.6112 Epoch 181/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6913 - mean_squared_error: 0.6542 - val_loss: 0.6507 - val_mean_squared_error: 0.6133 Epoch 182/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6881 - mean_squared_error: 0.6509 - val_loss: 0.6487 - val_mean_squared_error: 0.6117 Epoch 183/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6880 - mean_squared_error: 0.6511 - val_loss: 0.6486 - val_mean_squared_error: 0.6118 Epoch 184/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6884 - mean_squared_error: 0.6517 - val_loss: 0.6476 - val_mean_squared_error: 0.6112 Epoch 185/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6893 - mean_squared_error: 0.6528 - val_loss: 0.6492 - val_mean_squared_error: 0.6134 Epoch 186/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6907 - mean_squared_error: 0.6548 - val_loss: 0.6500 - val_mean_squared_error: 0.6137 Epoch 187/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6872 - mean_squared_error: 0.6510 - val_loss: 0.6470 - val_mean_squared_error: 0.6113 Epoch 188/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6862 - mean_squared_error: 0.6505 - val_loss: 0.6496 - val_mean_squared_error: 0.6136 Epoch 189/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6860 - mean_squared_error: 0.6502 - val_loss: 0.6466 - val_mean_squared_error: 0.6112 Epoch 190/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6871 - mean_squared_error: 0.6516 - val_loss: 0.6466 - val_mean_squared_error: 0.6116 Epoch 191/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6879 - mean_squared_error: 0.6528 - val_loss: 0.6478 - val_mean_squared_error: 0.6132 Epoch 192/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6907 - mean_squared_error: 0.6559 - val_loss: 0.6479 - val_mean_squared_error: 0.6135 Epoch 193/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6892 - mean_squared_error: 0.6546 - val_loss: 0.6470 - val_mean_squared_error: 0.6122 Epoch 194/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6862 - mean_squared_error: 0.6514 - val_loss: 0.6463 - val_mean_squared_error: 0.6121 Epoch 195/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6866 - mean_squared_error: 0.6524 - val_loss: 0.6458 - val_mean_squared_error: 0.6114 Epoch 196/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6875 - mean_squared_error: 0.6534 - val_loss: 0.6474 - val_mean_squared_error: 0.6130 Epoch 197/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6857 - mean_squared_error: 0.6515 - val_loss: 0.6450 - val_mean_squared_error: 0.6113 Epoch 198/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6842 - mean_squared_error: 0.6503 - val_loss: 0.6450 - val_mean_squared_error: 0.6115 Epoch 199/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6846 - mean_squared_error: 0.6512 - val_loss: 0.6498 - val_mean_squared_error: 0.6158 Epoch 200/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6856 - mean_squared_error: 0.6521 - val_loss: 0.6448 - val_mean_squared_error: 0.6114 Epoch 201/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6824 - mean_squared_error: 0.6491 - val_loss: 0.6471 - val_mean_squared_error: 0.6137 Epoch 202/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6845 - mean_squared_error: 0.6512 - val_loss: 0.6441 - val_mean_squared_error: 0.6113 Epoch 203/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6851 - mean_squared_error: 0.6521 - val_loss: 0.6444 - val_mean_squared_error: 0.6119 Epoch 204/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6845 - mean_squared_error: 0.6517 - val_loss: 0.6438 - val_mean_squared_error: 0.6113 Epoch 205/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6823 - mean_squared_error: 0.6499 - val_loss: 0.6438 - val_mean_squared_error: 0.6113 Epoch 206/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6830 - mean_squared_error: 0.6506 - val_loss: 0.6455 - val_mean_squared_error: 0.6130 Epoch 207/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6851 - mean_squared_error: 0.6529 - val_loss: 0.6462 - val_mean_squared_error: 0.6138 Epoch 208/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6860 - mean_squared_error: 0.6538 - val_loss: 0.6437 - val_mean_squared_error: 0.6117 Epoch 209/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6826 - mean_squared_error: 0.6508 - val_loss: 0.6443 - val_mean_squared_error: 0.6124 Epoch 210/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6844 - mean_squared_error: 0.6524 - val_loss: 0.6427 - val_mean_squared_error: 0.6113 Epoch 211/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6836 - mean_squared_error: 0.6523 - val_loss: 0.6450 - val_mean_squared_error: 0.6133 Epoch 212/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6831 - mean_squared_error: 0.6516 - val_loss: 0.6424 - val_mean_squared_error: 0.6112 Epoch 213/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6846 - mean_squared_error: 0.6533 - val_loss: 0.6441 - val_mean_squared_error: 0.6134 Epoch 214/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6842 - mean_squared_error: 0.6534 - val_loss: 0.6423 - val_mean_squared_error: 0.6114 Epoch 215/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6822 - mean_squared_error: 0.6513 - val_loss: 0.6421 - val_mean_squared_error: 0.6113 Epoch 216/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6802 - mean_squared_error: 0.6494 - val_loss: 0.6417 - val_mean_squared_error: 0.6112 Epoch 217/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6835 - mean_squared_error: 0.6529 - val_loss: 0.6418 - val_mean_squared_error: 0.6116 Epoch 218/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6809 - mean_squared_error: 0.6507 - val_loss: 0.6439 - val_mean_squared_error: 0.6134 Epoch 219/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6801 - mean_squared_error: 0.6498 - val_loss: 0.6412 - val_mean_squared_error: 0.6112 Epoch 220/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6825 - mean_squared_error: 0.6524 - val_loss: 0.6415 - val_mean_squared_error: 0.6118 Epoch 221/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6821 - mean_squared_error: 0.6523 - val_loss: 0.6446 - val_mean_squared_error: 0.6145 Epoch 222/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6809 - mean_squared_error: 0.6510 - val_loss: 0.6408 - val_mean_squared_error: 0.6112 Epoch 223/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6807 - mean_squared_error: 0.6512 - val_loss: 0.6410 - val_mean_squared_error: 0.6115 Epoch 224/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6789 - mean_squared_error: 0.6495 - val_loss: 0.6410 - val_mean_squared_error: 0.6116 Epoch 225/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6801 - mean_squared_error: 0.6508 - val_loss: 0.6403 - val_mean_squared_error: 0.6112 Epoch 226/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6796 - mean_squared_error: 0.6504 - val_loss: 0.6402 - val_mean_squared_error: 0.6113 Epoch 227/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6819 - mean_squared_error: 0.6529 - val_loss: 0.6419 - val_mean_squared_error: 0.6135 Epoch 228/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6807 - mean_squared_error: 0.6520 - val_loss: 0.6401 - val_mean_squared_error: 0.6113 Epoch 229/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6787 - mean_squared_error: 0.6498 - val_loss: 0.6405 - val_mean_squared_error: 0.6122 Epoch 230/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6819 - mean_squared_error: 0.6535 - val_loss: 0.6403 - val_mean_squared_error: 0.6122 Epoch 231/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6795 - mean_squared_error: 0.6512 - val_loss: 0.6396 - val_mean_squared_error: 0.6113 Epoch 232/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6793 - mean_squared_error: 0.6510 - val_loss: 0.6399 - val_mean_squared_error: 0.6117 Epoch 233/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6770 - mean_squared_error: 0.6489 - val_loss: 0.6397 - val_mean_squared_error: 0.6116 Epoch 234/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6793 - mean_squared_error: 0.6513 - val_loss: 0.6390 - val_mean_squared_error: 0.6113 Epoch 235/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6803 - mean_squared_error: 0.6524 - val_loss: 0.6390 - val_mean_squared_error: 0.6114 Epoch 236/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6790 - mean_squared_error: 0.6514 - val_loss: 0.6402 - val_mean_squared_error: 0.6124 Epoch 237/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6801 - mean_squared_error: 0.6525 - val_loss: 0.6387 - val_mean_squared_error: 0.6115 Epoch 238/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6810 - mean_squared_error: 0.6535 - val_loss: 0.6423 - val_mean_squared_error: 0.6155 Epoch 239/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6797 - mean_squared_error: 0.6528 - val_loss: 0.6441 - val_mean_squared_error: 0.6164 Epoch 240/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6794 - mean_squared_error: 0.6520 - val_loss: 0.6387 - val_mean_squared_error: 0.6116 Epoch 241/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6812 - mean_squared_error: 0.6540 - val_loss: 0.6381 - val_mean_squared_error: 0.6114 Epoch 242/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6791 - mean_squared_error: 0.6522 - val_loss: 0.6379 - val_mean_squared_error: 0.6113 Epoch 243/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6790 - mean_squared_error: 0.6525 - val_loss: 0.6398 - val_mean_squared_error: 0.6129 Epoch 244/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6757 - mean_squared_error: 0.6490 - val_loss: 0.6377 - val_mean_squared_error: 0.6113 Epoch 245/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6774 - mean_squared_error: 0.6508 - val_loss: 0.6388 - val_mean_squared_error: 0.6127 Epoch 246/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6775 - mean_squared_error: 0.6513 - val_loss: 0.6391 - val_mean_squared_error: 0.6126 Epoch 247/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6790 - mean_squared_error: 0.6528 - val_loss: 0.6457 - val_mean_squared_error: 0.6191 Epoch 248/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6794 - mean_squared_error: 0.6529 - val_loss: 0.6372 - val_mean_squared_error: 0.6113 Epoch 249/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6767 - mean_squared_error: 0.6508 - val_loss: 0.6372 - val_mean_squared_error: 0.6113 Epoch 250/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6781 - mean_squared_error: 0.6523 - val_loss: 0.6377 - val_mean_squared_error: 0.6118 Epoch 251/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6762 - mean_squared_error: 0.6506 - val_loss: 0.6404 - val_mean_squared_error: 0.6145 Epoch 252/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6785 - mean_squared_error: 0.6527 - val_loss: 0.6367 - val_mean_squared_error: 0.6112 Epoch 253/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6754 - mean_squared_error: 0.6501 - val_loss: 0.6376 - val_mean_squared_error: 0.6121 Epoch 254/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6754 - mean_squared_error: 0.6499 - val_loss: 0.6364 - val_mean_squared_error: 0.6112 Epoch 255/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6769 - mean_squared_error: 0.6517 - val_loss: 0.6364 - val_mean_squared_error: 0.6113 Epoch 256/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6775 - mean_squared_error: 0.6523 - val_loss: 0.6371 - val_mean_squared_error: 0.6123 Epoch 257/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6767 - mean_squared_error: 0.6519 - val_loss: 0.6364 - val_mean_squared_error: 0.6115 Epoch 258/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6758 - mean_squared_error: 0.6510 - val_loss: 0.6377 - val_mean_squared_error: 0.6127 Epoch 259/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6750 - mean_squared_error: 0.6501 - val_loss: 0.6360 - val_mean_squared_error: 0.6113 Epoch 260/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6740 - mean_squared_error: 0.6494 - val_loss: 0.6357 - val_mean_squared_error: 0.6112 Epoch 261/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6762 - mean_squared_error: 0.6518 - val_loss: 0.6360 - val_mean_squared_error: 0.6115 Epoch 262/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6759 - mean_squared_error: 0.6513 - val_loss: 0.6367 - val_mean_squared_error: 0.6127 Epoch 263/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6757 - mean_squared_error: 0.6516 - val_loss: 0.6380 - val_mean_squared_error: 0.6135 Epoch 264/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6757 - mean_squared_error: 0.6514 - val_loss: 0.6352 - val_mean_squared_error: 0.6112 Epoch 265/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6741 - mean_squared_error: 0.6500 - val_loss: 0.6352 - val_mean_squared_error: 0.6113 Epoch 266/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6777 - mean_squared_error: 0.6539 - val_loss: 0.6359 - val_mean_squared_error: 0.6119 Epoch 267/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6742 - mean_squared_error: 0.6502 - val_loss: 0.6350 - val_mean_squared_error: 0.6112 Epoch 268/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6739 - mean_squared_error: 0.6503 - val_loss: 0.6394 - val_mean_squared_error: 0.6153 Epoch 269/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6752 - mean_squared_error: 0.6514 - val_loss: 0.6357 - val_mean_squared_error: 0.6120 Epoch 270/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6743 - mean_squared_error: 0.6507 - val_loss: 0.6350 - val_mean_squared_error: 0.6115 Epoch 271/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6741 - mean_squared_error: 0.6508 - val_loss: 0.6391 - val_mean_squared_error: 0.6154 Epoch 272/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6732 - mean_squared_error: 0.6498 - val_loss: 0.6344 - val_mean_squared_error: 0.6112 Epoch 273/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6733 - mean_squared_error: 0.6501 - val_loss: 0.6349 - val_mean_squared_error: 0.6121 Epoch 274/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6755 - mean_squared_error: 0.6526 - val_loss: 0.6362 - val_mean_squared_error: 0.6130 Epoch 275/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6729 - mean_squared_error: 0.6497 - val_loss: 0.6354 - val_mean_squared_error: 0.6128 Epoch 276/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6749 - mean_squared_error: 0.6521 - val_loss: 0.6339 - val_mean_squared_error: 0.6112 Epoch 277/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6721 - mean_squared_error: 0.6493 - val_loss: 0.6341 - val_mean_squared_error: 0.6116 Epoch 278/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6746 - mean_squared_error: 0.6520 - val_loss: 0.6360 - val_mean_squared_error: 0.6138 Epoch 279/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6764 - mean_squared_error: 0.6539 - val_loss: 0.6338 - val_mean_squared_error: 0.6115 Epoch 280/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6727 - mean_squared_error: 0.6504 - val_loss: 0.6410 - val_mean_squared_error: 0.6181 Epoch 281/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6760 - mean_squared_error: 0.6533 - val_loss: 0.6334 - val_mean_squared_error: 0.6112 Epoch 282/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6778 - mean_squared_error: 0.6556 - val_loss: 0.6356 - val_mean_squared_error: 0.6131 Epoch 283/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6755 - mean_squared_error: 0.6533 - val_loss: 0.6337 - val_mean_squared_error: 0.6115 Epoch 284/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6741 - mean_squared_error: 0.6522 - val_loss: 0.6352 - val_mean_squared_error: 0.6130 Epoch 285/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6758 - mean_squared_error: 0.6537 - val_loss: 0.6331 - val_mean_squared_error: 0.6112 Epoch 286/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6731 - mean_squared_error: 0.6511 - val_loss: 0.6330 - val_mean_squared_error: 0.6112 Epoch 287/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6734 - mean_squared_error: 0.6517 - val_loss: 0.6335 - val_mean_squared_error: 0.6116 Epoch 288/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6723 - mean_squared_error: 0.6505 - val_loss: 0.6330 - val_mean_squared_error: 0.6113 Epoch 289/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6732 - mean_squared_error: 0.6515 - val_loss: 0.6347 - val_mean_squared_error: 0.6135 Epoch 290/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6729 - mean_squared_error: 0.6517 - val_loss: 0.6476 - val_mean_squared_error: 0.6254 Epoch 291/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6785 - mean_squared_error: 0.6568 - val_loss: 0.6327 - val_mean_squared_error: 0.6113 Epoch 292/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6716 - mean_squared_error: 0.6501 - val_loss: 0.6330 - val_mean_squared_error: 0.6119 Epoch 293/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6746 - mean_squared_error: 0.6534 - val_loss: 0.6341 - val_mean_squared_error: 0.6127 Epoch 294/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6725 - mean_squared_error: 0.6514 - val_loss: 0.6364 - val_mean_squared_error: 0.6150 Epoch 295/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6743 - mean_squared_error: 0.6530 - val_loss: 0.6371 - val_mean_squared_error: 0.6157 Epoch 296/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6748 - mean_squared_error: 0.6537 - val_loss: 0.6339 - val_mean_squared_error: 0.6127 Epoch 297/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6710 - mean_squared_error: 0.6499 - val_loss: 0.6324 - val_mean_squared_error: 0.6117 Epoch 298/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6721 - mean_squared_error: 0.6513 - val_loss: 0.6328 - val_mean_squared_error: 0.6119 Epoch 299/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6715 - mean_squared_error: 0.6507 - val_loss: 0.6319 - val_mean_squared_error: 0.6112 Epoch 300/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6718 - mean_squared_error: 0.6512 - val_loss: 0.6318 - val_mean_squared_error: 0.6114 Epoch 301/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6731 - mean_squared_error: 0.6526 - val_loss: 0.6316 - val_mean_squared_error: 0.6112 Epoch 302/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6708 - mean_squared_error: 0.6502 - val_loss: 0.6334 - val_mean_squared_error: 0.6134 Epoch 303/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6743 - mean_squared_error: 0.6542 - val_loss: 0.6363 - val_mean_squared_error: 0.6156 Epoch 304/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6728 - mean_squared_error: 0.6522 - val_loss: 0.6314 - val_mean_squared_error: 0.6112 Epoch 305/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6706 - mean_squared_error: 0.6503 - val_loss: 0.6322 - val_mean_squared_error: 0.6123 Epoch 306/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6734 - mean_squared_error: 0.6535 - val_loss: 0.6328 - val_mean_squared_error: 0.6125 Epoch 307/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6714 - mean_squared_error: 0.6515 - val_loss: 0.6344 - val_mean_squared_error: 0.6141 Epoch 308/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6728 - mean_squared_error: 0.6527 - val_loss: 0.6326 - val_mean_squared_error: 0.6125 Epoch 309/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6713 - mean_squared_error: 0.6513 - val_loss: 0.6310 - val_mean_squared_error: 0.6112 Epoch 310/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6708 - mean_squared_error: 0.6510 - val_loss: 0.6324 - val_mean_squared_error: 0.6125 Epoch 311/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6714 - mean_squared_error: 0.6516 - val_loss: 0.6319 - val_mean_squared_error: 0.6121 Epoch 312/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6702 - mean_squared_error: 0.6505 - val_loss: 0.6312 - val_mean_squared_error: 0.6115 Epoch 313/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6706 - mean_squared_error: 0.6510 - val_loss: 0.6309 - val_mean_squared_error: 0.6113 Epoch 314/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6718 - mean_squared_error: 0.6524 - val_loss: 0.6345 - val_mean_squared_error: 0.6147 Epoch 315/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6700 - mean_squared_error: 0.6502 - val_loss: 0.6344 - val_mean_squared_error: 0.6155 Epoch 316/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6743 - mean_squared_error: 0.6552 - val_loss: 0.6310 - val_mean_squared_error: 0.6120 Epoch 317/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6711 - mean_squared_error: 0.6519 - val_loss: 0.6304 - val_mean_squared_error: 0.6114 Epoch 318/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6708 - mean_squared_error: 0.6518 - val_loss: 0.6332 - val_mean_squared_error: 0.6138 Epoch 319/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6710 - mean_squared_error: 0.6518 - val_loss: 0.6320 - val_mean_squared_error: 0.6127 Epoch 320/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6701 - mean_squared_error: 0.6508 - val_loss: 0.6301 - val_mean_squared_error: 0.6112 Epoch 321/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6706 - mean_squared_error: 0.6515 - val_loss: 0.6326 - val_mean_squared_error: 0.6141 Epoch 322/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6726 - mean_squared_error: 0.6537 - val_loss: 0.6325 - val_mean_squared_error: 0.6141 Epoch 323/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6704 - mean_squared_error: 0.6518 - val_loss: 0.6319 - val_mean_squared_error: 0.6130 Epoch 324/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6695 - mean_squared_error: 0.6505 - val_loss: 0.6301 - val_mean_squared_error: 0.6115 Epoch 325/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6717 - mean_squared_error: 0.6529 - val_loss: 0.6300 - val_mean_squared_error: 0.6116 Epoch 326/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6703 - mean_squared_error: 0.6519 - val_loss: 0.6316 - val_mean_squared_error: 0.6128 Epoch 327/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6686 - mean_squared_error: 0.6500 - val_loss: 0.6300 - val_mean_squared_error: 0.6115 Epoch 328/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6694 - mean_squared_error: 0.6507 - val_loss: 0.6304 - val_mean_squared_error: 0.6122 Epoch 329/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6712 - mean_squared_error: 0.6529 - val_loss: 0.6296 - val_mean_squared_error: 0.6114 Epoch 330/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6693 - mean_squared_error: 0.6510 - val_loss: 0.6304 - val_mean_squared_error: 0.6120 Epoch 331/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6694 - mean_squared_error: 0.6510 - val_loss: 0.6295 - val_mean_squared_error: 0.6114 Epoch 332/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6710 - mean_squared_error: 0.6529 - val_loss: 0.6302 - val_mean_squared_error: 0.6120 Epoch 333/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6698 - mean_squared_error: 0.6517 - val_loss: 0.6300 - val_mean_squared_error: 0.6122 Epoch 334/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6714 - mean_squared_error: 0.6535 - val_loss: 0.6292 - val_mean_squared_error: 0.6112 Epoch 335/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6684 - mean_squared_error: 0.6505 - val_loss: 0.6295 - val_mean_squared_error: 0.6115 Epoch 336/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6689 - mean_squared_error: 0.6508 - val_loss: 0.6291 - val_mean_squared_error: 0.6113 Epoch 337/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6676 - mean_squared_error: 0.6498 - val_loss: 0.6301 - val_mean_squared_error: 0.6121 Epoch 338/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6691 - mean_squared_error: 0.6513 - val_loss: 0.6290 - val_mean_squared_error: 0.6113 Epoch 339/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6671 - mean_squared_error: 0.6494 - val_loss: 0.6288 - val_mean_squared_error: 0.6112 Epoch 340/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6677 - mean_squared_error: 0.6502 - val_loss: 0.6341 - val_mean_squared_error: 0.6161 Epoch 341/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6722 - mean_squared_error: 0.6545 - val_loss: 0.6292 - val_mean_squared_error: 0.6116 Epoch 342/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6698 - mean_squared_error: 0.6523 - val_loss: 0.6346 - val_mean_squared_error: 0.6167 Epoch 343/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6725 - mean_squared_error: 0.6549 - val_loss: 0.6293 - val_mean_squared_error: 0.6118 Epoch 344/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6694 - mean_squared_error: 0.6520 - val_loss: 0.6296 - val_mean_squared_error: 0.6121 Epoch 345/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6670 - mean_squared_error: 0.6495 - val_loss: 0.6321 - val_mean_squared_error: 0.6152 Epoch 346/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6689 - mean_squared_error: 0.6518 - val_loss: 0.6305 - val_mean_squared_error: 0.6131 Epoch 347/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6688 - mean_squared_error: 0.6515 - val_loss: 0.6286 - val_mean_squared_error: 0.6117 Epoch 348/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6686 - mean_squared_error: 0.6514 - val_loss: 0.6295 - val_mean_squared_error: 0.6127 Epoch 349/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6675 - mean_squared_error: 0.6507 - val_loss: 0.6321 - val_mean_squared_error: 0.6147 Epoch 350/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6688 - mean_squared_error: 0.6517 - val_loss: 0.6282 - val_mean_squared_error: 0.6113 Epoch 351/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6690 - mean_squared_error: 0.6520 - val_loss: 0.6281 - val_mean_squared_error: 0.6114 Epoch 352/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6689 - mean_squared_error: 0.6522 - val_loss: 0.6311 - val_mean_squared_error: 0.6139 Epoch 353/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6677 - mean_squared_error: 0.6507 - val_loss: 0.6287 - val_mean_squared_error: 0.6121 Epoch 354/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6674 - mean_squared_error: 0.6508 - val_loss: 0.6335 - val_mean_squared_error: 0.6163 Epoch 355/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6702 - mean_squared_error: 0.6533 - val_loss: 0.6282 - val_mean_squared_error: 0.6115 Epoch 356/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6670 - mean_squared_error: 0.6503 - val_loss: 0.6279 - val_mean_squared_error: 0.6113 Epoch 357/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6679 - mean_squared_error: 0.6513 - val_loss: 0.6277 - val_mean_squared_error: 0.6112 Epoch 358/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6679 - mean_squared_error: 0.6515 - val_loss: 0.6277 - val_mean_squared_error: 0.6113 Epoch 359/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6679 - mean_squared_error: 0.6515 - val_loss: 0.6289 - val_mean_squared_error: 0.6123 Epoch 360/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6689 - mean_squared_error: 0.6524 - val_loss: 0.6283 - val_mean_squared_error: 0.6118 Epoch 361/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6696 - mean_squared_error: 0.6533 - val_loss: 0.6283 - val_mean_squared_error: 0.6118 Epoch 362/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6677 - mean_squared_error: 0.6513 - val_loss: 0.6280 - val_mean_squared_error: 0.6116 Epoch 363/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6681 - mean_squared_error: 0.6518 - val_loss: 0.6279 - val_mean_squared_error: 0.6116 Epoch 364/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6682 - mean_squared_error: 0.6520 - val_loss: 0.6282 - val_mean_squared_error: 0.6119 Epoch 365/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6674 - mean_squared_error: 0.6510 - val_loss: 0.6282 - val_mean_squared_error: 0.6123 Epoch 366/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6672 - mean_squared_error: 0.6512 - val_loss: 0.6286 - val_mean_squared_error: 0.6124 Epoch 367/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6670 - mean_squared_error: 0.6508 - val_loss: 0.6279 - val_mean_squared_error: 0.6122 Epoch 368/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6665 - mean_squared_error: 0.6506 - val_loss: 0.6281 - val_mean_squared_error: 0.6120 Epoch 369/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6681 - mean_squared_error: 0.6520 - val_loss: 0.6271 - val_mean_squared_error: 0.6113 Epoch 370/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6697 - mean_squared_error: 0.6539 - val_loss: 0.6273 - val_mean_squared_error: 0.6117 Epoch 371/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6680 - mean_squared_error: 0.6523 - val_loss: 0.6274 - val_mean_squared_error: 0.6115 Epoch 372/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6667 - mean_squared_error: 0.6509 - val_loss: 0.6278 - val_mean_squared_error: 0.6120 Epoch 373/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6672 - mean_squared_error: 0.6516 - val_loss: 0.6320 - val_mean_squared_error: 0.6159 Epoch 374/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6671 - mean_squared_error: 0.6513 - val_loss: 0.6272 - val_mean_squared_error: 0.6115 Epoch 375/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6653 - mean_squared_error: 0.6497 - val_loss: 0.6268 - val_mean_squared_error: 0.6112 Epoch 376/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6668 - mean_squared_error: 0.6512 - val_loss: 0.6267 - val_mean_squared_error: 0.6113 Epoch 377/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6685 - mean_squared_error: 0.6529 - val_loss: 0.6278 - val_mean_squared_error: 0.6126 Epoch 378/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6668 - mean_squared_error: 0.6515 - val_loss: 0.6309 - val_mean_squared_error: 0.6151 Epoch 379/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6664 - mean_squared_error: 0.6508 - val_loss: 0.6265 - val_mean_squared_error: 0.6112 Epoch 380/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6659 - mean_squared_error: 0.6506 - val_loss: 0.6265 - val_mean_squared_error: 0.6112 Epoch 381/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6668 - mean_squared_error: 0.6515 - val_loss: 0.6270 - val_mean_squared_error: 0.6116 Epoch 382/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6680 - mean_squared_error: 0.6528 - val_loss: 0.6293 - val_mean_squared_error: 0.6138 Epoch 383/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6658 - mean_squared_error: 0.6504 - val_loss: 0.6278 - val_mean_squared_error: 0.6129 Epoch 384/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6683 - mean_squared_error: 0.6533 - val_loss: 0.6273 - val_mean_squared_error: 0.6121 Epoch 385/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6669 - mean_squared_error: 0.6517 - val_loss: 0.6264 - val_mean_squared_error: 0.6115 Epoch 386/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6670 - mean_squared_error: 0.6519 - val_loss: 0.6262 - val_mean_squared_error: 0.6112 Epoch 387/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6659 - mean_squared_error: 0.6509 - val_loss: 0.6262 - val_mean_squared_error: 0.6114 Epoch 388/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6671 - mean_squared_error: 0.6521 - val_loss: 0.6261 - val_mean_squared_error: 0.6113 Epoch 389/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6658 - mean_squared_error: 0.6511 - val_loss: 0.6316 - val_mean_squared_error: 0.6163 Epoch 390/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6679 - mean_squared_error: 0.6529 - val_loss: 0.6287 - val_mean_squared_error: 0.6136 Epoch 391/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6676 - mean_squared_error: 0.6527 - val_loss: 0.6264 - val_mean_squared_error: 0.6115 Epoch 392/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6667 - mean_squared_error: 0.6518 - val_loss: 0.6263 - val_mean_squared_error: 0.6117 Epoch 393/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6673 - mean_squared_error: 0.6526 - val_loss: 0.6259 - val_mean_squared_error: 0.6112 Epoch 394/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6656 - mean_squared_error: 0.6510 - val_loss: 0.6258 - val_mean_squared_error: 0.6112 Epoch 395/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6697 - mean_squared_error: 0.6550 - val_loss: 0.6258 - val_mean_squared_error: 0.6114 Epoch 396/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6665 - mean_squared_error: 0.6522 - val_loss: 0.6362 - val_mean_squared_error: 0.6211 Epoch 397/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6666 - mean_squared_error: 0.6518 - val_loss: 0.6263 - val_mean_squared_error: 0.6120 Epoch 398/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6666 - mean_squared_error: 0.6523 - val_loss: 0.6304 - val_mean_squared_error: 0.6156 Epoch 399/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6683 - mean_squared_error: 0.6536 - val_loss: 0.6260 - val_mean_squared_error: 0.6115 Epoch 400/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6650 - mean_squared_error: 0.6505 - val_loss: 0.6268 - val_mean_squared_error: 0.6126 Epoch 401/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6669 - mean_squared_error: 0.6527 - val_loss: 0.6280 - val_mean_squared_error: 0.6134 Epoch 402/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6685 - mean_squared_error: 0.6540 - val_loss: 0.6261 - val_mean_squared_error: 0.6117 Epoch 403/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6669 - mean_squared_error: 0.6527 - val_loss: 0.6265 - val_mean_squared_error: 0.6121 Epoch 404/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6654 - mean_squared_error: 0.6511 - val_loss: 0.6254 - val_mean_squared_error: 0.6112 Epoch 405/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6661 - mean_squared_error: 0.6518 - val_loss: 0.6254 - val_mean_squared_error: 0.6112 Epoch 406/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6651 - mean_squared_error: 0.6507 - val_loss: 0.6269 - val_mean_squared_error: 0.6130 Epoch 407/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6659 - mean_squared_error: 0.6518 - val_loss: 0.6258 - val_mean_squared_error: 0.6116 Epoch 408/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6638 - mean_squared_error: 0.6496 - val_loss: 0.6264 - val_mean_squared_error: 0.6126 Epoch 409/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6676 - mean_squared_error: 0.6537 - val_loss: 0.6252 - val_mean_squared_error: 0.6112 Epoch 410/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6650 - mean_squared_error: 0.6511 - val_loss: 0.6292 - val_mean_squared_error: 0.6149 Epoch 411/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6646 - mean_squared_error: 0.6505 - val_loss: 0.6255 - val_mean_squared_error: 0.6117 Epoch 412/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_squared_error: 0.6519 - val_loss: 0.6266 - val_mean_squared_error: 0.6125 Epoch 413/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6512 - val_loss: 0.6250 - val_mean_squared_error: 0.6113 Epoch 414/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6677 - mean_squared_error: 0.6539 - val_loss: 0.6252 - val_mean_squared_error: 0.6116 Epoch 415/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6656 - mean_squared_error: 0.6519 - val_loss: 0.6249 - val_mean_squared_error: 0.6112 Epoch 416/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6659 - mean_squared_error: 0.6521 - val_loss: 0.6253 - val_mean_squared_error: 0.6115 Epoch 417/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6637 - mean_squared_error: 0.6500 - val_loss: 0.6248 - val_mean_squared_error: 0.6112 Epoch 418/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6644 - mean_squared_error: 0.6507 - val_loss: 0.6250 - val_mean_squared_error: 0.6113 Epoch 419/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6647 - mean_squared_error: 0.6514 - val_loss: 0.6456 - val_mean_squared_error: 0.6312 Epoch 420/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6723 - mean_squared_error: 0.6582 - val_loss: 0.6253 - val_mean_squared_error: 0.6119 Epoch 421/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6642 - mean_squared_error: 0.6506 - val_loss: 0.6251 - val_mean_squared_error: 0.6115 Epoch 422/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6641 - mean_squared_error: 0.6505 - val_loss: 0.6252 - val_mean_squared_error: 0.6116 Epoch 423/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6634 - mean_squared_error: 0.6499 - val_loss: 0.6265 - val_mean_squared_error: 0.6128 Epoch 424/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6673 - mean_squared_error: 0.6538 - val_loss: 0.6271 - val_mean_squared_error: 0.6134 Epoch 425/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6657 - mean_squared_error: 0.6521 - val_loss: 0.6251 - val_mean_squared_error: 0.6117 Epoch 426/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6668 - mean_squared_error: 0.6534 - val_loss: 0.6247 - val_mean_squared_error: 0.6113 Epoch 427/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6639 - mean_squared_error: 0.6504 - val_loss: 0.6245 - val_mean_squared_error: 0.6113 Epoch 428/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6520 - val_loss: 0.6271 - val_mean_squared_error: 0.6136 Epoch 429/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6653 - mean_squared_error: 0.6519 - val_loss: 0.6245 - val_mean_squared_error: 0.6114 Epoch 430/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6667 - mean_squared_error: 0.6535 - val_loss: 0.6255 - val_mean_squared_error: 0.6122 Epoch 431/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6652 - mean_squared_error: 0.6519 - val_loss: 0.6245 - val_mean_squared_error: 0.6113 Epoch 432/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6647 - mean_squared_error: 0.6515 - val_loss: 0.6244 - val_mean_squared_error: 0.6115 Epoch 433/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6636 - mean_squared_error: 0.6507 - val_loss: 0.6319 - val_mean_squared_error: 0.6183 Epoch 434/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6643 - mean_squared_error: 0.6510 - val_loss: 0.6251 - val_mean_squared_error: 0.6123 Epoch 435/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6670 - mean_squared_error: 0.6540 - val_loss: 0.6249 - val_mean_squared_error: 0.6120 Epoch 436/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6655 - mean_squared_error: 0.6526 - val_loss: 0.6253 - val_mean_squared_error: 0.6121 Epoch 437/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6651 - mean_squared_error: 0.6522 - val_loss: 0.6310 - val_mean_squared_error: 0.6176 Epoch 438/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6653 - mean_squared_error: 0.6522 - val_loss: 0.6252 - val_mean_squared_error: 0.6121 Epoch 439/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6631 - mean_squared_error: 0.6502 - val_loss: 0.6299 - val_mean_squared_error: 0.6166 Epoch 440/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6672 - mean_squared_error: 0.6542 - val_loss: 0.6243 - val_mean_squared_error: 0.6114 Epoch 441/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6639 - mean_squared_error: 0.6511 - val_loss: 0.6272 - val_mean_squared_error: 0.6141 Epoch 442/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6627 - mean_squared_error: 0.6498 - val_loss: 0.6242 - val_mean_squared_error: 0.6116 Epoch 443/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6509 - val_loss: 0.6281 - val_mean_squared_error: 0.6150 Epoch 444/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6690 - mean_squared_error: 0.6561 - val_loss: 0.6266 - val_mean_squared_error: 0.6136 Epoch 445/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6658 - mean_squared_error: 0.6530 - val_loss: 0.6271 - val_mean_squared_error: 0.6142 Epoch 446/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6507 - val_loss: 0.6245 - val_mean_squared_error: 0.6121 Epoch 447/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6655 - mean_squared_error: 0.6529 - val_loss: 0.6240 - val_mean_squared_error: 0.6116 Epoch 448/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6644 - mean_squared_error: 0.6518 - val_loss: 0.6245 - val_mean_squared_error: 0.6118 Epoch 449/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6643 - mean_squared_error: 0.6516 - val_loss: 0.6244 - val_mean_squared_error: 0.6120 Epoch 450/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6641 - mean_squared_error: 0.6518 - val_loss: 0.6271 - val_mean_squared_error: 0.6143 Epoch 451/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6627 - mean_squared_error: 0.6500 - val_loss: 0.6320 - val_mean_squared_error: 0.6201 Epoch 452/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6698 - mean_squared_error: 0.6574 - val_loss: 0.6239 - val_mean_squared_error: 0.6116 Epoch 453/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6647 - mean_squared_error: 0.6523 - val_loss: 0.6266 - val_mean_squared_error: 0.6139 Epoch 454/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6661 - mean_squared_error: 0.6534 - val_loss: 0.6250 - val_mean_squared_error: 0.6129 Epoch 455/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6635 - mean_squared_error: 0.6512 - val_loss: 0.6235 - val_mean_squared_error: 0.6112 Epoch 456/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6631 - mean_squared_error: 0.6508 - val_loss: 0.6245 - val_mean_squared_error: 0.6120 Epoch 457/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6639 - mean_squared_error: 0.6514 - val_loss: 0.6253 - val_mean_squared_error: 0.6133 Epoch 458/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6610 - mean_squared_error: 0.6487 - val_loss: 0.6264 - val_mean_squared_error: 0.6139 Epoch 459/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6630 - mean_squared_error: 0.6507 - val_loss: 0.6239 - val_mean_squared_error: 0.6116 Epoch 460/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6664 - mean_squared_error: 0.6541 - val_loss: 0.6244 - val_mean_squared_error: 0.6124 Epoch 461/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6651 - mean_squared_error: 0.6528 - val_loss: 0.6235 - val_mean_squared_error: 0.6114 Epoch 462/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6655 - mean_squared_error: 0.6535 - val_loss: 0.6288 - val_mean_squared_error: 0.6163 Epoch 463/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6513 - val_loss: 0.6233 - val_mean_squared_error: 0.6112 Epoch 464/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6636 - mean_squared_error: 0.6516 - val_loss: 0.6253 - val_mean_squared_error: 0.6130 Epoch 465/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6639 - mean_squared_error: 0.6519 - val_loss: 0.6243 - val_mean_squared_error: 0.6121 Epoch 466/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6616 - mean_squared_error: 0.6493 - val_loss: 0.6331 - val_mean_squared_error: 0.6217 Epoch 467/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6681 - mean_squared_error: 0.6563 - val_loss: 0.6239 - val_mean_squared_error: 0.6118 Epoch 468/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6619 - mean_squared_error: 0.6499 - val_loss: 0.6232 - val_mean_squared_error: 0.6112 Epoch 469/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6668 - mean_squared_error: 0.6548 - val_loss: 0.6245 - val_mean_squared_error: 0.6128 Epoch 470/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6629 - mean_squared_error: 0.6510 - val_loss: 0.6255 - val_mean_squared_error: 0.6134 Epoch 471/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6626 - mean_squared_error: 0.6504 - val_loss: 0.6299 - val_mean_squared_error: 0.6186 Epoch 472/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6674 - mean_squared_error: 0.6556 - val_loss: 0.6231 - val_mean_squared_error: 0.6114 Epoch 473/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6642 - mean_squared_error: 0.6524 - val_loss: 0.6246 - val_mean_squared_error: 0.6126 Epoch 474/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6619 - mean_squared_error: 0.6500 - val_loss: 0.6231 - val_mean_squared_error: 0.6113 Epoch 475/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6642 - mean_squared_error: 0.6524 - val_loss: 0.6231 - val_mean_squared_error: 0.6113 Epoch 476/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6625 - mean_squared_error: 0.6508 - val_loss: 0.6260 - val_mean_squared_error: 0.6140 Epoch 477/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6636 - mean_squared_error: 0.6517 - val_loss: 0.6230 - val_mean_squared_error: 0.6114 Epoch 478/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6616 - mean_squared_error: 0.6500 - val_loss: 0.6271 - val_mean_squared_error: 0.6151 Epoch 479/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6617 - mean_squared_error: 0.6498 - val_loss: 0.6275 - val_mean_squared_error: 0.6163 Epoch 480/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6628 - mean_squared_error: 0.6514 - val_loss: 0.6346 - val_mean_squared_error: 0.6225 Epoch 481/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6695 - mean_squared_error: 0.6577 - val_loss: 0.6240 - val_mean_squared_error: 0.6123 Epoch 482/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6638 - mean_squared_error: 0.6522 - val_loss: 0.6241 - val_mean_squared_error: 0.6123 Epoch 483/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6628 - mean_squared_error: 0.6511 - val_loss: 0.6227 - val_mean_squared_error: 0.6112 Epoch 484/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6617 - mean_squared_error: 0.6503 - val_loss: 0.6259 - val_mean_squared_error: 0.6141 Epoch 485/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6622 - mean_squared_error: 0.6505 - val_loss: 0.6252 - val_mean_squared_error: 0.6141 Epoch 486/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6658 - mean_squared_error: 0.6544 - val_loss: 0.6229 - val_mean_squared_error: 0.6114 Epoch 487/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6618 - mean_squared_error: 0.6504 - val_loss: 0.6226 - val_mean_squared_error: 0.6112 Epoch 488/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6617 - mean_squared_error: 0.6503 - val_loss: 0.6241 - val_mean_squared_error: 0.6125 Epoch 489/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6658 - mean_squared_error: 0.6543 - val_loss: 0.6231 - val_mean_squared_error: 0.6119 Epoch 490/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6619 - mean_squared_error: 0.6505 - val_loss: 0.6225 - val_mean_squared_error: 0.6112 Epoch 491/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6650 - mean_squared_error: 0.6536 - val_loss: 0.6232 - val_mean_squared_error: 0.6121 Epoch 492/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6654 - mean_squared_error: 0.6542 - val_loss: 0.6319 - val_mean_squared_error: 0.6201 Epoch 493/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6638 - mean_squared_error: 0.6523 - val_loss: 0.6224 - val_mean_squared_error: 0.6112 Epoch 494/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6622 - mean_squared_error: 0.6510 - val_loss: 0.6227 - val_mean_squared_error: 0.6114 Epoch 495/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6628 - mean_squared_error: 0.6516 - val_loss: 0.6275 - val_mean_squared_error: 0.6159 Epoch 496/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6664 - mean_squared_error: 0.6551 - val_loss: 0.6257 - val_mean_squared_error: 0.6142 Epoch 497/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6635 - mean_squared_error: 0.6523 - val_loss: 0.6239 - val_mean_squared_error: 0.6125 Epoch 498/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6628 - mean_squared_error: 0.6517 - val_loss: 0.6242 - val_mean_squared_error: 0.6128 Epoch 499/500 9/9 [==============================] - 0s 2ms/step - loss: 0.6648 - mean_squared_error: 0.6534 - val_loss: 0.6227 - val_mean_squared_error: 0.6118 Epoch 500/500 9/9 [==============================] - 0s 3ms/step - loss: 0.6630 - mean_squared_error: 0.6520 - val_loss: 0.6227 - val_mean_squared_error: 0.6115 Total Time Taken is : -13.992571115493774
y_pred_reg_3=model_reg_3.predict(X_test).astype("int64")
print("The Accuracy of the model is : ",accuracy_score(y_test,y_pred_reg_3))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_test,y_pred_reg_3),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.41388888888888886
history=history_reg_3.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["mean_squared_error"])
ax.set_title("Training Mean Squared Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
#
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["val_loss"])
ax.set_title("Validation loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["val_mean_squared_error"])
ax.set_title("Validation Mean Squared Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'mean_squared_error', 'val_loss', 'val_mean_squared_error'])
Obviously, doing some PCA is not helping at all. Number of itreations with possible changes have been done but they haven't been documented here. Some more testing for a sample has been taken below.
One glaring thing is that data is not at all sufficient we want to train our model because batch size of 50 and 100 epochs has already cleaned the dataset which contains just 1599 input points. Hence we need more data to hit that above 80 accuracy.
#Freshly reloading the data again
X_train1, X_valid, y_train1, y_valid = train_test_split(data, data_org["Signal_Strength"], random_state=0)
###################################################################
#Categorical Neural Network
###################################################################
model_cat_1_test=k.Sequential()
#model_cat_1_test.add(Flatten(input_shape=(X_train.shape[1],)))
#model_cat_1_test.add(Reshape((784,),input_shape=(X_train.shape[0],X_train.shape[1],)))
model_cat_1_test.add(BatchNormalization())
model_cat_1_test.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dropout(0.2, input_shape=(60,)))
model_cat_1_test.add(Dense(30,activation="relu",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dropout(0.2, input_shape=(30,)))
#model_cat_1_test.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1_test.add(Dropout(0.4, input_shape=(60,)))
#model_cat_1_test.add(Dense(60,activation="relu",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1_test.add(Dropout(0.2, input_shape=(60,)))
model_cat_1_test.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dropout(0.4, input_shape=(60,)))
model_cat_1_test.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dropout(0.2, input_shape=(30,)))
model_cat_1_test.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dropout(0.2, input_shape=(30,)))
model_cat_1_test.add(Dense(15,activation="sigmoid",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1_test.add(Dense(9,activation="softmax"))
sgd = optimizers.SGD(lr = 0.01,momentum=0.3)
model_cat_1_test.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.CategoricalAccuracy())
t=time.time()
###################################################################
#
###################################################################
history_cat_1_test=model_cat_1_test.fit(X_train1,k.utils.to_categorical(y_train1),batch_size=50, epochs = 1000, verbose = 1)
print("Total Time Taken is : ",t-time.time())
Epoch 1/1000
WARNING:tensorflow:Layer batch_normalization_6 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
24/24 [==============================] - 0s 956us/step - loss: 0.2954 - categorical_accuracy: 0.0000e+00
Epoch 2/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2917 - categorical_accuracy: 0.0000e+00
Epoch 3/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2882 - categorical_accuracy: 0.0000e+00
Epoch 4/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2847 - categorical_accuracy: 0.0000e+00
Epoch 5/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2813 - categorical_accuracy: 0.0000e+00
Epoch 6/1000
24/24 [==============================] - 0s 956us/step - loss: 0.2781 - categorical_accuracy: 0.0000e+00
Epoch 7/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2749 - categorical_accuracy: 0.0000e+00
Epoch 8/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2718 - categorical_accuracy: 0.0000e+00
Epoch 9/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2688 - categorical_accuracy: 0.0000e+00
Epoch 10/1000
24/24 [==============================] - 0s 956us/step - loss: 0.2659 - categorical_accuracy: 0.0000e+00
Epoch 11/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2630 - categorical_accuracy: 0.0000e+00
Epoch 12/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2602 - categorical_accuracy: 0.0000e+00
Epoch 13/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2574 - categorical_accuracy: 0.0000e+00
Epoch 14/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2548 - categorical_accuracy: 0.0000e+00
Epoch 15/1000
24/24 [==============================] - 0s 914us/step - loss: 0.2521 - categorical_accuracy: 0.0000e+00
Epoch 16/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2495 - categorical_accuracy: 0.0000e+00
Epoch 17/1000
24/24 [==============================] - 0s 873us/step - loss: 0.2470 - categorical_accuracy: 0.0000e+00
Epoch 18/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2445 - categorical_accuracy: 0.0000e+00
Epoch 19/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2420 - categorical_accuracy: 0.0000e+00
Epoch 20/1000
24/24 [==============================] - 0s 873us/step - loss: 0.2396 - categorical_accuracy: 0.0000e+00
Epoch 21/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2373 - categorical_accuracy: 0.0000e+00
Epoch 22/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2350 - categorical_accuracy: 0.0000e+00
Epoch 23/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2326 - categorical_accuracy: 0.0000e+00
Epoch 24/1000
24/24 [==============================] - 0s 831us/step - loss: 0.2304 - categorical_accuracy: 0.0000e+00
Epoch 25/1000
24/24 [==============================] - 0s 790us/step - loss: 0.2282 - categorical_accuracy: 0.0000e+00
Epoch 26/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2260 - categorical_accuracy: 0.0000e+00
Epoch 27/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2239 - categorical_accuracy: 0.0000e+00
Epoch 28/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2217 - categorical_accuracy: 0.0000e+00
Epoch 29/1000
24/24 [==============================] - 0s 956us/step - loss: 0.2196 - categorical_accuracy: 0.0000e+00
Epoch 30/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2176 - categorical_accuracy: 0.0000e+00
Epoch 31/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2156 - categorical_accuracy: 0.0000e+00
Epoch 32/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2135 - categorical_accuracy: 0.0000e+00
Epoch 33/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2115 - categorical_accuracy: 0.0108
Epoch 34/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2096 - categorical_accuracy: 0.1560
Epoch 35/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2077 - categorical_accuracy: 0.3545
Epoch 36/1000
24/24 [==============================] - 0s 997us/step - loss: 0.2058 - categorical_accuracy: 0.4195
Epoch 37/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.2040 - categorical_accuracy: 0.4262
Epoch 38/1000
24/24 [==============================] - 0s 956us/step - loss: 0.2021 - categorical_accuracy: 0.4270
Epoch 39/1000
24/24 [==============================] - 0s 956us/step - loss: 0.2003 - categorical_accuracy: 0.4270
Epoch 40/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1985 - categorical_accuracy: 0.4270
Epoch 41/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1968 - categorical_accuracy: 0.4270
Epoch 42/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1951 - categorical_accuracy: 0.4270
Epoch 43/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1933 - categorical_accuracy: 0.4270
Epoch 44/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1917 - categorical_accuracy: 0.4270
Epoch 45/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1900 - categorical_accuracy: 0.4270
Epoch 46/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1884 - categorical_accuracy: 0.4270
Epoch 47/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1868 - categorical_accuracy: 0.4270
Epoch 48/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1852 - categorical_accuracy: 0.4270
Epoch 49/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1836 - categorical_accuracy: 0.4270
Epoch 50/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1821 - categorical_accuracy: 0.4270
Epoch 51/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1806 - categorical_accuracy: 0.4270
Epoch 52/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1791 - categorical_accuracy: 0.4270
Epoch 53/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1776 - categorical_accuracy: 0.4270
Epoch 54/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1762 - categorical_accuracy: 0.4270
Epoch 55/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1748 - categorical_accuracy: 0.4270
Epoch 56/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1734 - categorical_accuracy: 0.4270
Epoch 57/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1720 - categorical_accuracy: 0.4270
Epoch 58/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1706 - categorical_accuracy: 0.4270
Epoch 59/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1693 - categorical_accuracy: 0.4270
Epoch 60/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1680 - categorical_accuracy: 0.4270
Epoch 61/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1667 - categorical_accuracy: 0.4270
Epoch 62/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1654 - categorical_accuracy: 0.4270
Epoch 63/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1641 - categorical_accuracy: 0.4270
Epoch 64/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1629 - categorical_accuracy: 0.4270
Epoch 65/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1617 - categorical_accuracy: 0.4270
Epoch 66/1000
24/24 [==============================] - 0s 790us/step - loss: 0.1604 - categorical_accuracy: 0.4270
Epoch 67/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1593 - categorical_accuracy: 0.4270
Epoch 68/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1581 - categorical_accuracy: 0.4270
Epoch 69/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1569 - categorical_accuracy: 0.4270
Epoch 70/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1558 - categorical_accuracy: 0.4270
Epoch 71/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1547 - categorical_accuracy: 0.4270
Epoch 72/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1536 - categorical_accuracy: 0.4270
Epoch 73/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1525 - categorical_accuracy: 0.4270
Epoch 74/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1515 - categorical_accuracy: 0.4270
Epoch 75/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1504 - categorical_accuracy: 0.4270
Epoch 76/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1494 - categorical_accuracy: 0.4270
Epoch 77/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1484 - categorical_accuracy: 0.4270
Epoch 78/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1473 - categorical_accuracy: 0.4270
Epoch 79/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1463 - categorical_accuracy: 0.4270
Epoch 80/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1454 - categorical_accuracy: 0.4270
Epoch 81/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1444 - categorical_accuracy: 0.4270
Epoch 82/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1434 - categorical_accuracy: 0.4270
Epoch 83/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1425 - categorical_accuracy: 0.4270
Epoch 84/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1416 - categorical_accuracy: 0.4270
Epoch 85/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1407 - categorical_accuracy: 0.4270
Epoch 86/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1398 - categorical_accuracy: 0.4270
Epoch 87/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1389 - categorical_accuracy: 0.4270
Epoch 88/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1380 - categorical_accuracy: 0.4270
Epoch 89/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1371 - categorical_accuracy: 0.4270
Epoch 90/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1363 - categorical_accuracy: 0.4270
Epoch 91/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1354 - categorical_accuracy: 0.4270
Epoch 92/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1346 - categorical_accuracy: 0.4270
Epoch 93/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1338 - categorical_accuracy: 0.4270
Epoch 94/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1329 - categorical_accuracy: 0.4270
Epoch 95/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1321 - categorical_accuracy: 0.4270
Epoch 96/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1313 - categorical_accuracy: 0.4270
Epoch 97/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1305 - categorical_accuracy: 0.4270
Epoch 98/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1298 - categorical_accuracy: 0.4270
Epoch 99/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1290 - categorical_accuracy: 0.4270
Epoch 100/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1283 - categorical_accuracy: 0.4270
Epoch 101/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1276 - categorical_accuracy: 0.4270
Epoch 102/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1268 - categorical_accuracy: 0.4270
Epoch 103/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1261 - categorical_accuracy: 0.4270
Epoch 104/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1253 - categorical_accuracy: 0.4270
Epoch 105/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1246 - categorical_accuracy: 0.4270
Epoch 106/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1239 - categorical_accuracy: 0.4270
Epoch 107/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1232 - categorical_accuracy: 0.4270
Epoch 108/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1226 - categorical_accuracy: 0.4270
Epoch 109/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1219 - categorical_accuracy: 0.4270
Epoch 110/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1212 - categorical_accuracy: 0.4270
Epoch 111/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1205 - categorical_accuracy: 0.4270
Epoch 112/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1199 - categorical_accuracy: 0.4270
Epoch 113/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1193 - categorical_accuracy: 0.4270
Epoch 114/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1187 - categorical_accuracy: 0.4270
Epoch 115/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1180 - categorical_accuracy: 0.4270
Epoch 116/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1174 - categorical_accuracy: 0.4270
Epoch 117/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1168 - categorical_accuracy: 0.4270
Epoch 118/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1162 - categorical_accuracy: 0.4270
Epoch 119/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1156 - categorical_accuracy: 0.4270
Epoch 120/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1150 - categorical_accuracy: 0.4270
Epoch 121/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1144 - categorical_accuracy: 0.4270
Epoch 122/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1139 - categorical_accuracy: 0.4270
Epoch 123/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1133 - categorical_accuracy: 0.4270
Epoch 124/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1127 - categorical_accuracy: 0.4270
Epoch 125/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1122 - categorical_accuracy: 0.4270
Epoch 126/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1116 - categorical_accuracy: 0.4270
Epoch 127/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1111 - categorical_accuracy: 0.4270
Epoch 128/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1106 - categorical_accuracy: 0.4270
Epoch 129/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1101 - categorical_accuracy: 0.4270
Epoch 130/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1095 - categorical_accuracy: 0.4270
Epoch 131/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1090 - categorical_accuracy: 0.4270
Epoch 132/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1085 - categorical_accuracy: 0.4270
Epoch 133/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1080 - categorical_accuracy: 0.4270
Epoch 134/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1076 - categorical_accuracy: 0.4270
Epoch 135/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1070 - categorical_accuracy: 0.4270
Epoch 136/1000
24/24 [==============================] - 0s 914us/step - loss: 0.1066 - categorical_accuracy: 0.4270
Epoch 137/1000
24/24 [==============================] - 0s 873us/step - loss: 0.1061 - categorical_accuracy: 0.4270
Epoch 138/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1056 - categorical_accuracy: 0.4270
Epoch 139/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1052 - categorical_accuracy: 0.4270
Epoch 140/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1047 - categorical_accuracy: 0.4270
Epoch 141/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1043 - categorical_accuracy: 0.4270
Epoch 142/1000
24/24 [==============================] - 0s 790us/step - loss: 0.1039 - categorical_accuracy: 0.4270
Epoch 143/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1034 - categorical_accuracy: 0.4270
Epoch 144/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1030 - categorical_accuracy: 0.4270
Epoch 145/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1026 - categorical_accuracy: 0.4270
Epoch 146/1000
24/24 [==============================] - 0s 790us/step - loss: 0.1021 - categorical_accuracy: 0.4270
Epoch 147/1000
24/24 [==============================] - 0s 831us/step - loss: 0.1018 - categorical_accuracy: 0.4270
Epoch 148/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.1014 - categorical_accuracy: 0.4270
Epoch 149/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1009 - categorical_accuracy: 0.4270
Epoch 150/1000
24/24 [==============================] - 0s 956us/step - loss: 0.1006 - categorical_accuracy: 0.4270
Epoch 151/1000
24/24 [==============================] - 0s 997us/step - loss: 0.1002 - categorical_accuracy: 0.4270
Epoch 152/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0998 - categorical_accuracy: 0.4270
Epoch 153/1000
24/24 [==============================] - ETA: 0s - loss: 0.1047 - categorical_accuracy: 0.38 - 0s 956us/step - loss: 0.0994 - categorical_accuracy: 0.4270
Epoch 154/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0991 - categorical_accuracy: 0.4270
Epoch 155/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0987 - categorical_accuracy: 0.4270
Epoch 156/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0983 - categorical_accuracy: 0.4270
Epoch 157/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0980 - categorical_accuracy: 0.4270
Epoch 158/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0976 - categorical_accuracy: 0.4270
Epoch 159/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0973 - categorical_accuracy: 0.4270
Epoch 160/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0969 - categorical_accuracy: 0.4270
Epoch 161/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0966 - categorical_accuracy: 0.4270
Epoch 162/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0963 - categorical_accuracy: 0.4270
Epoch 163/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0959 - categorical_accuracy: 0.4270
Epoch 164/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0956 - categorical_accuracy: 0.4270
Epoch 165/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0953 - categorical_accuracy: 0.4270
Epoch 166/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0950 - categorical_accuracy: 0.4270
Epoch 167/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0947 - categorical_accuracy: 0.4270
Epoch 168/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0944 - categorical_accuracy: 0.4270
Epoch 169/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0941 - categorical_accuracy: 0.4270
Epoch 170/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0938 - categorical_accuracy: 0.4270
Epoch 171/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0935 - categorical_accuracy: 0.4270
Epoch 172/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0933 - categorical_accuracy: 0.4270
Epoch 173/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0930 - categorical_accuracy: 0.4270
Epoch 174/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0927 - categorical_accuracy: 0.4270
Epoch 175/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0924 - categorical_accuracy: 0.4270
Epoch 176/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0921 - categorical_accuracy: 0.4270
Epoch 177/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0919 - categorical_accuracy: 0.4270
Epoch 178/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0916 - categorical_accuracy: 0.4270
Epoch 179/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0914 - categorical_accuracy: 0.4270
Epoch 180/1000
24/24 [==============================] - ETA: 0s - loss: 0.0906 - categorical_accuracy: 0.46 - 0s 956us/step - loss: 0.0911 - categorical_accuracy: 0.4270
Epoch 181/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0909 - categorical_accuracy: 0.4270
Epoch 182/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0906 - categorical_accuracy: 0.4270
Epoch 183/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0904 - categorical_accuracy: 0.4270
Epoch 184/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0902 - categorical_accuracy: 0.4270
Epoch 185/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0899 - categorical_accuracy: 0.4270
Epoch 186/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0897 - categorical_accuracy: 0.4270
Epoch 187/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0895 - categorical_accuracy: 0.4270
Epoch 188/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0893 - categorical_accuracy: 0.4270
Epoch 189/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0891 - categorical_accuracy: 0.4270
Epoch 190/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0888 - categorical_accuracy: 0.4270
Epoch 191/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0886 - categorical_accuracy: 0.4270
Epoch 192/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0884 - categorical_accuracy: 0.4270
Epoch 193/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0882 - categorical_accuracy: 0.4270
Epoch 194/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0880 - categorical_accuracy: 0.4270
Epoch 195/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0878 - categorical_accuracy: 0.4270
Epoch 196/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0876 - categorical_accuracy: 0.4270
Epoch 197/1000
24/24 [==============================] - ETA: 0s - loss: 0.0851 - categorical_accuracy: 0.44 - 0s 914us/step - loss: 0.0874 - categorical_accuracy: 0.4270
Epoch 198/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0872 - categorical_accuracy: 0.4270
Epoch 199/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0870 - categorical_accuracy: 0.4270
Epoch 200/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0868 - categorical_accuracy: 0.4270
Epoch 201/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0866 - categorical_accuracy: 0.4270
Epoch 202/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0864 - categorical_accuracy: 0.4270
Epoch 203/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0862 - categorical_accuracy: 0.4270
Epoch 204/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0861 - categorical_accuracy: 0.4270
Epoch 205/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0859 - categorical_accuracy: 0.4270
Epoch 206/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0857 - categorical_accuracy: 0.4270
Epoch 207/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0856 - categorical_accuracy: 0.4270
Epoch 208/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0854 - categorical_accuracy: 0.4270
Epoch 209/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0852 - categorical_accuracy: 0.4270
Epoch 210/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0850 - categorical_accuracy: 0.4270
Epoch 211/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0849 - categorical_accuracy: 0.4270
Epoch 212/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0847 - categorical_accuracy: 0.4270
Epoch 213/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0846 - categorical_accuracy: 0.4270
Epoch 214/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0844 - categorical_accuracy: 0.4270
Epoch 215/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0843 - categorical_accuracy: 0.4270
Epoch 216/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0841 - categorical_accuracy: 0.4270
Epoch 217/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0840 - categorical_accuracy: 0.4270
Epoch 218/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0838 - categorical_accuracy: 0.4270
Epoch 219/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0837 - categorical_accuracy: 0.4270
Epoch 220/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0835 - categorical_accuracy: 0.4270
Epoch 221/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0834 - categorical_accuracy: 0.4270
Epoch 222/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0833 - categorical_accuracy: 0.4270
Epoch 223/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0831 - categorical_accuracy: 0.4270
Epoch 224/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0830 - categorical_accuracy: 0.4270
Epoch 225/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0829 - categorical_accuracy: 0.4270
Epoch 226/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0827 - categorical_accuracy: 0.4270
Epoch 227/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0826 - categorical_accuracy: 0.4270
Epoch 228/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0825 - categorical_accuracy: 0.4270
Epoch 229/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0824 - categorical_accuracy: 0.4270
Epoch 230/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0822 - categorical_accuracy: 0.4270
Epoch 231/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0821 - categorical_accuracy: 0.4270
Epoch 232/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0820 - categorical_accuracy: 0.4270
Epoch 233/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0819 - categorical_accuracy: 0.4270
Epoch 234/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0817 - categorical_accuracy: 0.4270
Epoch 235/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0816 - categorical_accuracy: 0.4270
Epoch 236/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0815 - categorical_accuracy: 0.4270
Epoch 237/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0814 - categorical_accuracy: 0.4270
Epoch 238/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0813 - categorical_accuracy: 0.4270
Epoch 239/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0812 - categorical_accuracy: 0.4270
Epoch 240/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0811 - categorical_accuracy: 0.4270
Epoch 241/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0810 - categorical_accuracy: 0.4270
Epoch 242/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0809 - categorical_accuracy: 0.4270
Epoch 243/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0808 - categorical_accuracy: 0.4270
Epoch 244/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0807 - categorical_accuracy: 0.4270
Epoch 245/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0806 - categorical_accuracy: 0.4270
Epoch 246/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0805 - categorical_accuracy: 0.4270
Epoch 247/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0804 - categorical_accuracy: 0.4270
Epoch 248/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0803 - categorical_accuracy: 0.4270
Epoch 249/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0802 - categorical_accuracy: 0.4270
Epoch 250/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0801 - categorical_accuracy: 0.4270
Epoch 251/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0800 - categorical_accuracy: 0.4270
Epoch 252/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0799 - categorical_accuracy: 0.4270
Epoch 253/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0798 - categorical_accuracy: 0.4270
Epoch 254/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 255/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0796 - categorical_accuracy: 0.4270
Epoch 256/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 257/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 258/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0794 - categorical_accuracy: 0.4270
Epoch 259/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0793 - categorical_accuracy: 0.4270
Epoch 260/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0792 - categorical_accuracy: 0.4270
Epoch 261/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0791 - categorical_accuracy: 0.4270
Epoch 262/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0790 - categorical_accuracy: 0.4270
Epoch 263/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0790 - categorical_accuracy: 0.4270
Epoch 264/1000
24/24 [==============================] - 0s 917us/step - loss: 0.0789 - categorical_accuracy: 0.4270
Epoch 265/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0788 - categorical_accuracy: 0.4270
Epoch 266/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0787 - categorical_accuracy: 0.4270
Epoch 267/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0787 - categorical_accuracy: 0.4270
Epoch 268/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0786 - categorical_accuracy: 0.4270
Epoch 269/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0785 - categorical_accuracy: 0.4270
Epoch 270/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0784 - categorical_accuracy: 0.4270
Epoch 271/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0784 - categorical_accuracy: 0.4270
Epoch 272/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0783 - categorical_accuracy: 0.4270
Epoch 273/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0782 - categorical_accuracy: 0.4270
Epoch 274/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0782 - categorical_accuracy: 0.4270
Epoch 275/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0781 - categorical_accuracy: 0.4270
Epoch 276/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0780 - categorical_accuracy: 0.4270
Epoch 277/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0780 - categorical_accuracy: 0.4270
Epoch 278/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0779 - categorical_accuracy: 0.4270
Epoch 279/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 280/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 281/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0777 - categorical_accuracy: 0.4270
Epoch 282/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0777 - categorical_accuracy: 0.4270
Epoch 283/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0776 - categorical_accuracy: 0.4270
Epoch 284/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0775 - categorical_accuracy: 0.4270
Epoch 285/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0775 - categorical_accuracy: 0.4270
Epoch 286/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 287/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 288/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0773 - categorical_accuracy: 0.4270
Epoch 289/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0772 - categorical_accuracy: 0.4270
Epoch 290/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0772 - categorical_accuracy: 0.4270
Epoch 291/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 292/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 293/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0770 - categorical_accuracy: 0.4270
Epoch 294/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0770 - categorical_accuracy: 0.4270
Epoch 295/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 296/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 297/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 298/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 299/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 300/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 301/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0766 - categorical_accuracy: 0.4270
Epoch 302/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0766 - categorical_accuracy: 0.4270
Epoch 303/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 304/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 305/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 306/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 307/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 308/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 309/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 310/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 311/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 312/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 313/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 314/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 315/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 316/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 317/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 318/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 319/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 320/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 321/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 322/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 323/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 324/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 325/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 326/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 327/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 328/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0755 - categorical_accuracy: 0.4270
Epoch 329/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0755 - categorical_accuracy: 0.4270
Epoch 330/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0755 - categorical_accuracy: 0.4270
Epoch 331/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0754 - categorical_accuracy: 0.4270
Epoch 332/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0754 - categorical_accuracy: 0.4270
Epoch 333/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0754 - categorical_accuracy: 0.4270
Epoch 334/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0754 - categorical_accuracy: 0.4270
Epoch 335/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0753 - categorical_accuracy: 0.4270
Epoch 336/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0753 - categorical_accuracy: 0.4270
Epoch 337/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0752 - categorical_accuracy: 0.4270
Epoch 338/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0752 - categorical_accuracy: 0.4270
Epoch 339/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0752 - categorical_accuracy: 0.4270
Epoch 340/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0752 - categorical_accuracy: 0.4270
Epoch 341/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0751 - categorical_accuracy: 0.4270
Epoch 342/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0751 - categorical_accuracy: 0.4270
Epoch 343/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0751 - categorical_accuracy: 0.4270
Epoch 344/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0751 - categorical_accuracy: 0.4270
Epoch 345/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0750 - categorical_accuracy: 0.4270
Epoch 346/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0750 - categorical_accuracy: 0.4270
Epoch 347/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0750 - categorical_accuracy: 0.4270
Epoch 348/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0749 - categorical_accuracy: 0.4270
Epoch 349/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0749 - categorical_accuracy: 0.4270
Epoch 350/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0749 - categorical_accuracy: 0.4270
Epoch 351/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0749 - categorical_accuracy: 0.4270
Epoch 352/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0748 - categorical_accuracy: 0.4270
Epoch 353/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0748 - categorical_accuracy: 0.4270
Epoch 354/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0748 - categorical_accuracy: 0.4270
Epoch 355/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0748 - categorical_accuracy: 0.4270
Epoch 356/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0748 - categorical_accuracy: 0.4270
Epoch 357/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0747 - categorical_accuracy: 0.4270
Epoch 358/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0747 - categorical_accuracy: 0.4270
Epoch 359/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0747 - categorical_accuracy: 0.4270
Epoch 360/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0747 - categorical_accuracy: 0.4270
Epoch 361/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0746 - categorical_accuracy: 0.4270
Epoch 362/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0746 - categorical_accuracy: 0.4270
Epoch 363/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0746 - categorical_accuracy: 0.4270
Epoch 364/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0746 - categorical_accuracy: 0.4270
Epoch 365/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0745 - categorical_accuracy: 0.4270
Epoch 366/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0745 - categorical_accuracy: 0.4270
Epoch 367/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0745 - categorical_accuracy: 0.4270
Epoch 368/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0745 - categorical_accuracy: 0.4270
Epoch 369/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0745 - categorical_accuracy: 0.4270
Epoch 370/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 371/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 372/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 373/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 374/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 375/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0744 - categorical_accuracy: 0.4270
Epoch 376/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0743 - categorical_accuracy: 0.4270
Epoch 377/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0743 - categorical_accuracy: 0.4270
Epoch 378/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0743 - categorical_accuracy: 0.4270
Epoch 379/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0743 - categorical_accuracy: 0.4270
Epoch 380/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0743 - categorical_accuracy: 0.4270
Epoch 381/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 382/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 383/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 384/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 385/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 386/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0742 - categorical_accuracy: 0.4270
Epoch 387/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 388/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 389/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 390/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 391/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 392/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0741 - categorical_accuracy: 0.4270
Epoch 393/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 394/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 395/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 396/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 397/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 398/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 399/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0740 - categorical_accuracy: 0.4270
Epoch 400/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 401/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 402/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 403/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 404/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 405/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 406/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0739 - categorical_accuracy: 0.4270
Epoch 407/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 408/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 409/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 410/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 411/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 412/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 413/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 414/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0738 - categorical_accuracy: 0.4270
Epoch 415/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 416/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 417/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 418/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 419/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 420/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 421/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 422/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 423/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 424/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0737 - categorical_accuracy: 0.4270
Epoch 425/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 426/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 427/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 428/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 429/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 430/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 431/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 432/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 433/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0736 - categorical_accuracy: 0.4270
Epoch 434/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 435/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 436/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 437/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 438/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 439/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 440/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 441/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 442/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 443/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 444/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 445/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0735 - categorical_accuracy: 0.4270
Epoch 446/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 447/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 448/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 449/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 450/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 451/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 452/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 453/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 454/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 455/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 456/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 457/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0734 - categorical_accuracy: 0.4270
Epoch 458/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 459/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 460/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 461/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 462/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 463/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 464/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 465/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 466/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 467/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 468/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 469/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 470/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 471/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0733 - categorical_accuracy: 0.4270
Epoch 472/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 473/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 474/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 475/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 476/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 477/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 478/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 479/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 480/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 481/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 482/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 483/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 484/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 485/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 486/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 487/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 488/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0732 - categorical_accuracy: 0.4270
Epoch 489/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 490/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 491/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 492/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 493/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 494/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 495/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 496/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 497/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 498/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 499/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 500/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 501/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 502/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 503/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 504/1000
24/24 [==============================] - ETA: 0s - loss: 0.0719 - categorical_accuracy: 0.38 - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 505/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 506/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 507/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 508/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 509/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0731 - categorical_accuracy: 0.4270
Epoch 510/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 511/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 512/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 513/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 514/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 515/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 516/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 517/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 518/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 519/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 520/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 521/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 522/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 523/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 524/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 525/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 526/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 527/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 528/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 529/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 530/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 531/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 532/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0730 - categorical_accuracy: 0.4270
Epoch 533/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 534/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 535/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 536/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 537/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 538/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 539/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 540/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 541/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 542/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 543/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 544/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 545/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 546/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 547/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 548/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 549/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 550/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 551/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 552/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 553/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 554/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 555/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 556/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 557/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 558/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 559/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 560/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 561/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 562/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 563/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0729 - categorical_accuracy: 0.4270
Epoch 564/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 565/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 566/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 567/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 568/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 569/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 570/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 571/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 572/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 573/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 574/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 575/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 576/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 577/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 578/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 579/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 580/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 581/1000
24/24 [==============================] - 0s 916us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 582/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 583/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 584/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 585/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 586/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 587/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 588/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 589/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 590/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 591/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 592/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 593/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 594/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 595/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 596/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 597/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 598/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 599/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 600/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 601/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0728 - categorical_accuracy: 0.4270
Epoch 602/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 603/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 604/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 605/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 606/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 607/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 608/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 609/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 610/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 611/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 612/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 613/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 614/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 615/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 616/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 617/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 618/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 619/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 620/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 621/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 622/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 623/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 624/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 625/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 626/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 627/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 628/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 629/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 630/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 631/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 632/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 633/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 634/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 635/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 636/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 637/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 638/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 639/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 640/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 641/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 642/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 643/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 644/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 645/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 646/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 647/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 648/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 649/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 650/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 651/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0727 - categorical_accuracy: 0.4270
Epoch 652/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 653/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 654/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 655/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 656/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 657/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 658/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 659/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 660/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 661/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 662/1000
24/24 [==============================] - ETA: 0s - loss: 0.0691 - categorical_accuracy: 0.48 - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 663/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 664/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 665/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 666/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 667/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 668/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 669/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 670/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 671/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 672/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 673/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 674/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 675/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 676/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 677/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 678/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 679/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 680/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 681/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 682/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 683/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 684/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 685/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 686/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 687/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 688/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 689/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 690/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 691/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 692/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 693/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 694/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 695/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 696/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 697/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 698/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 699/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 700/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 701/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 702/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 703/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 704/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 705/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 706/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 707/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 708/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 709/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 710/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 711/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 712/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 713/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 714/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 715/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 716/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0726 - categorical_accuracy: 0.4270
Epoch 717/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 718/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 719/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 720/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 721/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 722/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 723/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 724/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 725/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 726/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 727/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 728/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 729/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 730/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 731/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 732/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 733/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 734/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 735/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 736/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 737/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 738/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 739/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 740/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 741/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 742/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 743/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 744/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 745/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 746/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 747/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 748/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 749/1000
24/24 [==============================] - ETA: 0s - loss: 0.0695 - categorical_accuracy: 0.42 - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 750/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 751/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 752/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 753/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 754/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 755/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 756/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 757/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 758/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 759/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 760/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 761/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 762/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 763/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 764/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 765/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 766/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 767/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 768/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 769/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 770/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 771/1000
24/24 [==============================] - ETA: 0s - loss: 0.0752 - categorical_accuracy: 0.44 - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 772/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 773/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 774/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 775/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 776/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 777/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 778/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 779/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 780/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 781/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 782/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 783/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 784/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 785/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 786/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 787/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 788/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 789/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 790/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 791/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 792/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 793/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 794/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 795/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 796/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0725 - categorical_accuracy: 0.4270
Epoch 797/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 798/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 799/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 800/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 801/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 802/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 803/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 804/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 805/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 806/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 807/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 808/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 809/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 810/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 811/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 812/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 813/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 814/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 815/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 816/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 817/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 818/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 819/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 820/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 821/1000
24/24 [==============================] - 0s 916us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 822/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 823/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 824/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 825/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 826/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 827/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 828/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 829/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 830/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 831/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 832/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 833/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 834/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 835/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 836/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 837/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 838/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 839/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 840/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 841/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 842/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 843/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 844/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 845/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 846/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 847/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 848/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 849/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 850/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 851/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 852/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 853/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 854/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 855/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 856/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 857/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 858/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 859/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 860/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 861/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 862/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 863/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 864/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 865/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 866/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 867/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 868/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 869/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 870/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 871/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 872/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 873/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 874/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 875/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 876/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 877/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 878/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 879/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 880/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 881/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 882/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 883/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 884/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 885/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 886/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 887/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 888/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 889/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 890/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 891/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 892/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 893/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 894/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 895/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 896/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 897/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 898/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 899/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 900/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 901/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 902/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 903/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0724 - categorical_accuracy: 0.4270
Epoch 904/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 905/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 906/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 907/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 908/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 909/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 910/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 911/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 912/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 913/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 914/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 915/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 916/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 917/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 918/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 919/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 920/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 921/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 922/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 923/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 924/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 925/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 926/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 927/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 928/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 929/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 930/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 931/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 932/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 933/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 934/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 935/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 936/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 937/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 938/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 939/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 940/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 941/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 942/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 943/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 944/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 945/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 946/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 947/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 948/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 949/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 950/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 951/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 952/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 953/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 954/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 955/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 956/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 957/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 958/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 959/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 960/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 961/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 962/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 963/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 964/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 965/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 966/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 967/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 968/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 969/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 970/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 971/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 972/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 973/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 974/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 975/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 976/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 977/1000
24/24 [==============================] - 0s 914us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 978/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 979/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 980/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 981/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 982/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 983/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 984/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 985/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 986/1000
24/24 [==============================] - 0s 873us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 987/1000
24/24 [==============================] - 0s 831us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 988/1000
24/24 [==============================] - 0s 790us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 989/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 990/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 991/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 992/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 993/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 994/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 995/1000
24/24 [==============================] - 0s 1ms/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 996/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 997/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 998/1000
24/24 [==============================] - 0s 956us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 999/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Epoch 1000/1000
24/24 [==============================] - 0s 997us/step - loss: 0.0723 - categorical_accuracy: 0.4270
Total Time Taken is : -26.57990312576294
history=history_cat_1_test.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["categorical_accuracy"])
ax.set_title("Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'categorical_accuracy'])
Here we have decreased the batch size and increased the number of epochs, but still we couldn't hit a peak accuracy of greater than 0.45. Let us try the same with regression model where we got initial accuracy above 50.
###################################################################
#Regressional Neural Network
###################################################################
model_reg_1_test=k.Sequential()
model_reg_1_test.add(BatchNormalization(input_shape=(X_train1.shape[1],)))
model_reg_1_test.add(Flatten())
model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
model_reg_1_test.add(Dense(30,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.5, input_shape=(50,)))
#model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
#model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dropout(0.5, input_shape=(30,)))
model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dense(1))
sgd = optimizers.SGD(lr = 0.01,momentum=0.6)
model_reg_1_test.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.MeanSquaredError())
###################################################################
#
###################################################################
t=time.time()
history_reg_1_test=model_reg_1_test.fit(X_train1,y_train1,batch_size=50, epochs = 1000) #add verbose later
print("Total Time Taken is : ",t-time.time())
Epoch 1/1000 24/24 [==============================] - 0s 790us/step - loss: 7.4158 - mean_squared_error: 7.3408 Epoch 2/1000 24/24 [==============================] - 0s 831us/step - loss: 0.7568 - mean_squared_error: 0.6720 Epoch 3/1000 24/24 [==============================] - 0s 831us/step - loss: 0.7536 - mean_squared_error: 0.6702 Epoch 4/1000 24/24 [==============================] - 0s 914us/step - loss: 0.7499 - mean_squared_error: 0.6682 Epoch 5/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7497 - mean_squared_error: 0.6695 Epoch 6/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7467 - mean_squared_error: 0.6679 Epoch 7/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7459 - mean_squared_error: 0.6687 Epoch 8/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7440 - mean_squared_error: 0.6682 Epoch 9/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7466 - mean_squared_error: 0.6719 Epoch 10/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7443 - mean_squared_error: 0.6712 Epoch 11/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7401 - mean_squared_error: 0.6683 Epoch 12/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7416 - mean_squared_error: 0.6710 Epoch 13/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7364 - mean_squared_error: 0.6669 Epoch 14/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7395 - mean_squared_error: 0.6714 Epoch 15/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7319 - mean_squared_error: 0.6648 Epoch 16/1000 24/24 [==============================] - 0s 706us/step - loss: 0.7320 - mean_squared_error: 0.6660 Epoch 17/1000 24/24 [==============================] - 0s 665us/step - loss: 0.7309 - mean_squared_error: 0.6660 Epoch 18/1000 24/24 [==============================] - 0s 665us/step - loss: 0.7321 - mean_squared_error: 0.6684 Epoch 19/1000 24/24 [==============================] - 0s 665us/step - loss: 0.7275 - mean_squared_error: 0.6646 Epoch 20/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7292 - mean_squared_error: 0.6676 Epoch 21/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7244 - mean_squared_error: 0.6634 Epoch 22/1000 24/24 [==============================] - 0s 748us/step - loss: 0.7246 - mean_squared_error: 0.6647 Epoch 23/1000 24/24 [==============================] - 0s 748us/step - loss: 0.7211 - mean_squared_error: 0.6619 Epoch 24/1000 24/24 [==============================] - 0s 748us/step - loss: 0.7205 - mean_squared_error: 0.6621 Epoch 25/1000 24/24 [==============================] - 0s 748us/step - loss: 0.7176 - mean_squared_error: 0.6597 Epoch 26/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7110 - mean_squared_error: 0.6537 Epoch 27/1000 24/24 [==============================] - 0s 790us/step - loss: 0.7051 - mean_squared_error: 0.6480 Epoch 28/1000 24/24 [==============================] - 0s 748us/step - loss: 0.7044 - mean_squared_error: 0.6476 Epoch 29/1000 24/24 [==============================] - 0s 873us/step - loss: 0.6872 - mean_squared_error: 0.6299 Epoch 30/1000 24/24 [==============================] - 0s 790us/step - loss: 0.6423 - mean_squared_error: 0.5839 Epoch 31/1000 24/24 [==============================] - 0s 790us/step - loss: 0.5769 - mean_squared_error: 0.5167 Epoch 32/1000 24/24 [==============================] - 0s 831us/step - loss: 0.4811 - mean_squared_error: 0.4171 Epoch 33/1000 24/24 [==============================] - 0s 790us/step - loss: 0.4361 - mean_squared_error: 0.3696 Epoch 34/1000 24/24 [==============================] - 0s 831us/step - loss: 0.4311 - mean_squared_error: 0.3645 Epoch 35/1000 24/24 [==============================] - 0s 789us/step - loss: 0.4265 - mean_squared_error: 0.3605 Epoch 36/1000 24/24 [==============================] - 0s 873us/step - loss: 0.4079 - mean_squared_error: 0.3422 Epoch 37/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3965 - mean_squared_error: 0.3319 Epoch 38/1000 24/24 [==============================] - 0s 706us/step - loss: 0.4045 - mean_squared_error: 0.3395 Epoch 39/1000 24/24 [==============================] - 0s 748us/step - loss: 0.4028 - mean_squared_error: 0.3387 Epoch 40/1000 24/24 [==============================] - 0s 706us/step - loss: 0.4039 - mean_squared_error: 0.3402 Epoch 41/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3943 - mean_squared_error: 0.3312 Epoch 42/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3949 - mean_squared_error: 0.3326 Epoch 43/1000 24/24 [==============================] - 0s 706us/step - loss: 0.4164 - mean_squared_error: 0.3547 Epoch 44/1000 24/24 [==============================] - 0s 706us/step - loss: 0.4020 - mean_squared_error: 0.3408 Epoch 45/1000 24/24 [==============================] - 0s 665us/step - loss: 0.4128 - mean_squared_error: 0.3516 Epoch 46/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3797 - mean_squared_error: 0.3192 Epoch 47/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3821 - mean_squared_error: 0.3220 Epoch 48/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3982 - mean_squared_error: 0.3384 Epoch 49/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3843 - mean_squared_error: 0.3252 Epoch 50/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3709 - mean_squared_error: 0.3119 Epoch 51/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3737 - mean_squared_error: 0.3154 Epoch 52/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3727 - mean_squared_error: 0.3146 Epoch 53/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3781 - mean_squared_error: 0.3203 Epoch 54/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3734 - mean_squared_error: 0.3161 Epoch 55/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3720 - mean_squared_error: 0.3147 Epoch 56/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3762 - mean_squared_error: 0.3191 Epoch 57/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3693 - mean_squared_error: 0.3125 Epoch 58/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3755 - mean_squared_error: 0.3194 Epoch 59/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3804 - mean_squared_error: 0.3239 Epoch 60/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3647 - mean_squared_error: 0.3085 Epoch 61/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3749 - mean_squared_error: 0.3191 Epoch 62/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3659 - mean_squared_error: 0.3101 Epoch 63/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3780 - mean_squared_error: 0.3225 Epoch 64/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3806 - mean_squared_error: 0.3253 Epoch 65/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3568 - mean_squared_error: 0.3014 Epoch 66/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3468 - mean_squared_error: 0.2921 Epoch 67/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3491 - mean_squared_error: 0.2947 Epoch 68/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3515 - mean_squared_error: 0.2971 Epoch 69/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3528 - mean_squared_error: 0.2987 Epoch 70/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3585 - mean_squared_error: 0.3042 Epoch 71/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3625 - mean_squared_error: 0.3087 Epoch 72/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3492 - mean_squared_error: 0.2952 Epoch 73/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3540 - mean_squared_error: 0.3002 Epoch 74/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3589 - mean_squared_error: 0.3053 Epoch 75/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3510 - mean_squared_error: 0.2981 Epoch 76/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3380 - mean_squared_error: 0.2848 Epoch 77/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3681 - mean_squared_error: 0.3152 Epoch 78/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3430 - mean_squared_error: 0.2901 Epoch 79/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3407 - mean_squared_error: 0.2881 Epoch 80/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3584 - mean_squared_error: 0.3057 Epoch 81/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3524 - mean_squared_error: 0.3003 Epoch 82/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3392 - mean_squared_error: 0.2868 Epoch 83/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3299 - mean_squared_error: 0.2775 Epoch 84/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3553 - mean_squared_error: 0.3030 Epoch 85/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3502 - mean_squared_error: 0.2980 Epoch 86/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3451 - mean_squared_error: 0.2933 Epoch 87/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3333 - mean_squared_error: 0.2812 Epoch 88/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3388 - mean_squared_error: 0.2866 Epoch 89/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3392 - mean_squared_error: 0.2874 Epoch 90/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3396 - mean_squared_error: 0.2881 Epoch 91/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3302 - mean_squared_error: 0.2783 Epoch 92/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3415 - mean_squared_error: 0.2900 Epoch 93/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3385 - mean_squared_error: 0.2867 Epoch 94/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3482 - mean_squared_error: 0.2964 Epoch 95/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3275 - mean_squared_error: 0.2760 Epoch 96/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3427 - mean_squared_error: 0.2915 Epoch 97/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3337 - mean_squared_error: 0.2823 Epoch 98/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3312 - mean_squared_error: 0.2798 Epoch 99/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3237 - mean_squared_error: 0.2728 Epoch 100/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3333 - mean_squared_error: 0.2820 Epoch 101/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3354 - mean_squared_error: 0.2841 Epoch 102/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3140 - mean_squared_error: 0.2625 Epoch 103/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3310 - mean_squared_error: 0.2801 Epoch 104/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3209 - mean_squared_error: 0.2700 Epoch 105/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3316 - mean_squared_error: 0.2808 Epoch 106/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3313 - mean_squared_error: 0.2804 Epoch 107/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3342 - mean_squared_error: 0.2839 Epoch 108/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3318 - mean_squared_error: 0.2812 Epoch 109/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3224 - mean_squared_error: 0.2721 Epoch 110/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3280 - mean_squared_error: 0.2776 Epoch 111/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3110 - mean_squared_error: 0.2607 Epoch 112/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3277 - mean_squared_error: 0.2775 Epoch 113/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3316 - mean_squared_error: 0.2816 Epoch 114/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3342 - mean_squared_error: 0.2840 Epoch 115/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3362 - mean_squared_error: 0.2860 Epoch 116/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3217 - mean_squared_error: 0.2714 Epoch 117/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3224 - mean_squared_error: 0.2721 Epoch 118/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3208 - mean_squared_error: 0.2705 Epoch 119/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3302 - mean_squared_error: 0.2803 Epoch 120/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3237 - mean_squared_error: 0.2737 Epoch 121/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3183 - mean_squared_error: 0.2684 Epoch 122/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3120 - mean_squared_error: 0.2622 Epoch 123/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3430 - mean_squared_error: 0.2932 Epoch 124/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3260 - mean_squared_error: 0.2760 Epoch 125/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3147 - mean_squared_error: 0.2649 Epoch 126/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3236 - mean_squared_error: 0.2737 Epoch 127/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3218 - mean_squared_error: 0.2719 Epoch 128/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3263 - mean_squared_error: 0.2767 Epoch 129/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3267 - mean_squared_error: 0.2770 Epoch 130/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3350 - mean_squared_error: 0.2855 Epoch 131/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3098 - mean_squared_error: 0.2603 Epoch 132/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3191 - mean_squared_error: 0.2699 Epoch 133/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3306 - mean_squared_error: 0.2810 Epoch 134/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3038 - mean_squared_error: 0.2544 Epoch 135/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3155 - mean_squared_error: 0.2663 Epoch 136/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3062 - mean_squared_error: 0.2567 Epoch 137/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3199 - mean_squared_error: 0.2705 Epoch 138/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3390 - mean_squared_error: 0.2897 Epoch 139/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3355 - mean_squared_error: 0.2866 Epoch 140/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3250 - mean_squared_error: 0.2759 Epoch 141/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3174 - mean_squared_error: 0.2683 Epoch 142/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3062 - mean_squared_error: 0.2571 Epoch 143/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3071 - mean_squared_error: 0.2581 Epoch 144/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3315 - mean_squared_error: 0.2827 Epoch 145/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3255 - mean_squared_error: 0.2762 Epoch 146/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3102 - mean_squared_error: 0.2611 Epoch 147/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3156 - mean_squared_error: 0.2665 Epoch 148/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3125 - mean_squared_error: 0.2639 Epoch 149/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3094 - mean_squared_error: 0.2603 Epoch 150/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3217 - mean_squared_error: 0.2731 Epoch 151/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3401 - mean_squared_error: 0.2917 Epoch 152/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3193 - mean_squared_error: 0.2706 Epoch 153/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2923 - mean_squared_error: 0.2437 Epoch 154/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3133 - mean_squared_error: 0.2648 Epoch 155/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3102 - mean_squared_error: 0.2618 Epoch 156/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2966 - mean_squared_error: 0.2478 Epoch 157/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3071 - mean_squared_error: 0.2585 Epoch 158/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3101 - mean_squared_error: 0.2615 Epoch 159/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3171 - mean_squared_error: 0.2688 Epoch 160/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3241 - mean_squared_error: 0.2754 Epoch 161/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3156 - mean_squared_error: 0.2673 Epoch 162/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3029 - mean_squared_error: 0.2546 Epoch 163/1000 24/24 [==============================] - 0s 667us/step - loss: 0.3146 - mean_squared_error: 0.2659 Epoch 164/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3082 - mean_squared_error: 0.2597 Epoch 165/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3074 - mean_squared_error: 0.2586 Epoch 166/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3101 - mean_squared_error: 0.2616 Epoch 167/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3029 - mean_squared_error: 0.2545 Epoch 168/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3088 - mean_squared_error: 0.2604 Epoch 169/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3166 - mean_squared_error: 0.2686 Epoch 170/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3146 - mean_squared_error: 0.2669 Epoch 171/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3142 - mean_squared_error: 0.2666 Epoch 172/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3237 - mean_squared_error: 0.2758 Epoch 173/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3113 - mean_squared_error: 0.2636 Epoch 174/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3226 - mean_squared_error: 0.2748 Epoch 175/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3163 - mean_squared_error: 0.2683 Epoch 176/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3074 - mean_squared_error: 0.2593 Epoch 177/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3068 - mean_squared_error: 0.2586 Epoch 178/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3166 - mean_squared_error: 0.2683 Epoch 179/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3092 - mean_squared_error: 0.2612 Epoch 180/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3126 - mean_squared_error: 0.2643 Epoch 181/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3008 - mean_squared_error: 0.2528 Epoch 182/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3143 - mean_squared_error: 0.2661 Epoch 183/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3065 - mean_squared_error: 0.2583 Epoch 184/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2998 - mean_squared_error: 0.2518 Epoch 185/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3148 - mean_squared_error: 0.2668 Epoch 186/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3087 - mean_squared_error: 0.2607 Epoch 187/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3089 - mean_squared_error: 0.2610 Epoch 188/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3118 - mean_squared_error: 0.2640 Epoch 189/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2915 - mean_squared_error: 0.2436 Epoch 190/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3187 - mean_squared_error: 0.2708 Epoch 191/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2948 - mean_squared_error: 0.2469 Epoch 192/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2923 - mean_squared_error: 0.2445 Epoch 193/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3213 - mean_squared_error: 0.2734 Epoch 194/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2934 - mean_squared_error: 0.2454 Epoch 195/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2979 - mean_squared_error: 0.2500 Epoch 196/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3023 - mean_squared_error: 0.2544 Epoch 197/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2998 - mean_squared_error: 0.2519 Epoch 198/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3099 - mean_squared_error: 0.2619 Epoch 199/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3171 - mean_squared_error: 0.2695 Epoch 200/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3131 - mean_squared_error: 0.2651 Epoch 201/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3014 - mean_squared_error: 0.2536 Epoch 202/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3079 - mean_squared_error: 0.2599 Epoch 203/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3013 - mean_squared_error: 0.2534 Epoch 204/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3006 - mean_squared_error: 0.2528 Epoch 205/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3089 - mean_squared_error: 0.2609 Epoch 206/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3085 - mean_squared_error: 0.2609 Epoch 207/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2976 - mean_squared_error: 0.2501 Epoch 208/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3131 - mean_squared_error: 0.2653 Epoch 209/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2988 - mean_squared_error: 0.2509 Epoch 210/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3009 - mean_squared_error: 0.2533 Epoch 211/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3048 - mean_squared_error: 0.2570 Epoch 212/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3143 - mean_squared_error: 0.2670 Epoch 213/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3190 - mean_squared_error: 0.2715 Epoch 214/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3070 - mean_squared_error: 0.2597 Epoch 215/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2958 - mean_squared_error: 0.2484 Epoch 216/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2973 - mean_squared_error: 0.2497 Epoch 217/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2942 - mean_squared_error: 0.2469 Epoch 218/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3315 - mean_squared_error: 0.2842 Epoch 219/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3187 - mean_squared_error: 0.2716 Epoch 220/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3044 - mean_squared_error: 0.2573 Epoch 221/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3206 - mean_squared_error: 0.2733 Epoch 222/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3277 - mean_squared_error: 0.2805 Epoch 223/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3179 - mean_squared_error: 0.2710 Epoch 224/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3063 - mean_squared_error: 0.2595 Epoch 225/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3094 - mean_squared_error: 0.2626 Epoch 226/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3017 - mean_squared_error: 0.2550 Epoch 227/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3135 - mean_squared_error: 0.2668 Epoch 228/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3083 - mean_squared_error: 0.2618 Epoch 229/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3079 - mean_squared_error: 0.2614 Epoch 230/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2917 - mean_squared_error: 0.2450 Epoch 231/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2979 - mean_squared_error: 0.2516 Epoch 232/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3064 - mean_squared_error: 0.2596 Epoch 233/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2924 - mean_squared_error: 0.2456 Epoch 234/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3092 - mean_squared_error: 0.2625 Epoch 235/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3013 - mean_squared_error: 0.2549 Epoch 236/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3017 - mean_squared_error: 0.2551 Epoch 237/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2955 - mean_squared_error: 0.2491 Epoch 238/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2828 - mean_squared_error: 0.2366 Epoch 239/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2887 - mean_squared_error: 0.2421 Epoch 240/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2904 - mean_squared_error: 0.2438 Epoch 241/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3061 - mean_squared_error: 0.2596 Epoch 242/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3014 - mean_squared_error: 0.2547 Epoch 243/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2938 - mean_squared_error: 0.2477 Epoch 244/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3178 - mean_squared_error: 0.2715 Epoch 245/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3098 - mean_squared_error: 0.2638 Epoch 246/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3066 - mean_squared_error: 0.2605 Epoch 247/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3003 - mean_squared_error: 0.2540 Epoch 248/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3101 - mean_squared_error: 0.2639 Epoch 249/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3040 - mean_squared_error: 0.2576 Epoch 250/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3100 - mean_squared_error: 0.2638 Epoch 251/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3040 - mean_squared_error: 0.2579 Epoch 252/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3144 - mean_squared_error: 0.2684 Epoch 253/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3008 - mean_squared_error: 0.2549 Epoch 254/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3073 - mean_squared_error: 0.2611 Epoch 255/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3022 - mean_squared_error: 0.2561 Epoch 256/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3057 - mean_squared_error: 0.2595 Epoch 257/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2884 - mean_squared_error: 0.2421 Epoch 258/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3026 - mean_squared_error: 0.2564 Epoch 259/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3038 - mean_squared_error: 0.2575 Epoch 260/1000 24/24 [==============================] - 0s 707us/step - loss: 0.3021 - mean_squared_error: 0.2561 Epoch 261/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2982 - mean_squared_error: 0.2522 Epoch 262/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3031 - mean_squared_error: 0.2572 Epoch 263/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3136 - mean_squared_error: 0.2679 Epoch 264/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2992 - mean_squared_error: 0.2537 Epoch 265/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3031 - mean_squared_error: 0.2574 Epoch 266/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2910 - mean_squared_error: 0.2455 Epoch 267/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3001 - mean_squared_error: 0.2543 Epoch 268/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3002 - mean_squared_error: 0.2546 Epoch 269/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3107 - mean_squared_error: 0.2651 Epoch 270/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3067 - mean_squared_error: 0.2611 Epoch 271/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2980 - mean_squared_error: 0.2527 Epoch 272/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3134 - mean_squared_error: 0.2679 Epoch 273/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3039 - mean_squared_error: 0.2583 Epoch 274/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3025 - mean_squared_error: 0.2571 Epoch 275/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3072 - mean_squared_error: 0.2620 Epoch 276/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2904 - mean_squared_error: 0.2451 Epoch 277/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3105 - mean_squared_error: 0.2652 Epoch 278/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2963 - mean_squared_error: 0.2511 Epoch 279/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2903 - mean_squared_error: 0.2446 Epoch 280/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2986 - mean_squared_error: 0.2530 Epoch 281/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3084 - mean_squared_error: 0.2627 Epoch 282/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2911 - mean_squared_error: 0.2453 Epoch 283/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3098 - mean_squared_error: 0.2646 Epoch 284/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3015 - mean_squared_error: 0.2560 Epoch 285/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3057 - mean_squared_error: 0.2601 Epoch 286/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3031 - mean_squared_error: 0.2575 Epoch 287/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3050 - mean_squared_error: 0.2597 Epoch 288/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2978 - mean_squared_error: 0.2523 Epoch 289/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2994 - mean_squared_error: 0.2538 Epoch 290/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3032 - mean_squared_error: 0.2576 Epoch 291/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3066 - mean_squared_error: 0.2610 Epoch 292/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2898 - mean_squared_error: 0.2444 Epoch 293/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3037 - mean_squared_error: 0.2581 Epoch 294/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3001 - mean_squared_error: 0.2548 Epoch 295/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3029 - mean_squared_error: 0.2575 Epoch 296/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3094 - mean_squared_error: 0.2641 Epoch 297/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2939 - mean_squared_error: 0.2486 Epoch 298/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3074 - mean_squared_error: 0.2618 Epoch 299/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3010 - mean_squared_error: 0.2555 Epoch 300/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2834 - mean_squared_error: 0.2380 Epoch 301/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2939 - mean_squared_error: 0.2481 Epoch 302/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2903 - mean_squared_error: 0.2448 Epoch 303/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3064 - mean_squared_error: 0.2611 Epoch 304/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2866 - mean_squared_error: 0.2413 Epoch 305/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3059 - mean_squared_error: 0.2606 Epoch 306/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2905 - mean_squared_error: 0.2451 Epoch 307/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2984 - mean_squared_error: 0.2532 Epoch 308/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2941 - mean_squared_error: 0.2490 Epoch 309/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3122 - mean_squared_error: 0.2673 Epoch 310/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2990 - mean_squared_error: 0.2539 Epoch 311/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3080 - mean_squared_error: 0.2627 Epoch 312/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2994 - mean_squared_error: 0.2545 Epoch 313/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2900 - mean_squared_error: 0.2448 Epoch 314/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3064 - mean_squared_error: 0.2613 Epoch 315/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2954 - mean_squared_error: 0.2502 Epoch 316/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3024 - mean_squared_error: 0.2574 Epoch 317/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2982 - mean_squared_error: 0.2531 Epoch 318/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2960 - mean_squared_error: 0.2513 Epoch 319/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3069 - mean_squared_error: 0.2619 Epoch 320/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2917 - mean_squared_error: 0.2466 Epoch 321/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2938 - mean_squared_error: 0.2488 Epoch 322/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3017 - mean_squared_error: 0.2567 Epoch 323/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2803 - mean_squared_error: 0.2354 Epoch 324/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2927 - mean_squared_error: 0.2479 Epoch 325/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3050 - mean_squared_error: 0.2602 Epoch 326/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3050 - mean_squared_error: 0.2601 Epoch 327/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3030 - mean_squared_error: 0.2586 Epoch 328/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3004 - mean_squared_error: 0.2558 Epoch 329/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2996 - mean_squared_error: 0.2548 Epoch 330/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2996 - mean_squared_error: 0.2550 Epoch 331/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3063 - mean_squared_error: 0.2615 Epoch 332/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3070 - mean_squared_error: 0.2623 Epoch 333/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3035 - mean_squared_error: 0.2585 Epoch 334/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3158 - mean_squared_error: 0.2711 Epoch 335/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3054 - mean_squared_error: 0.2609 Epoch 336/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3073 - mean_squared_error: 0.2627 Epoch 337/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2980 - mean_squared_error: 0.2536 Epoch 338/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3080 - mean_squared_error: 0.2636 Epoch 339/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3120 - mean_squared_error: 0.2675 Epoch 340/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3056 - mean_squared_error: 0.2610 Epoch 341/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3031 - mean_squared_error: 0.2585 Epoch 342/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2998 - mean_squared_error: 0.2551 Epoch 343/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2984 - mean_squared_error: 0.2538 Epoch 344/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2937 - mean_squared_error: 0.2492 Epoch 345/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2931 - mean_squared_error: 0.2489 Epoch 346/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2895 - mean_squared_error: 0.2453 Epoch 347/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3207 - mean_squared_error: 0.2767 Epoch 348/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3028 - mean_squared_error: 0.2586 Epoch 349/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2867 - mean_squared_error: 0.2424 Epoch 350/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3017 - mean_squared_error: 0.2576 Epoch 351/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3216 - mean_squared_error: 0.2774 Epoch 352/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2837 - mean_squared_error: 0.2396 Epoch 353/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2905 - mean_squared_error: 0.2462 Epoch 354/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2884 - mean_squared_error: 0.2440 Epoch 355/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2848 - mean_squared_error: 0.2404 Epoch 356/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3003 - mean_squared_error: 0.2561 Epoch 357/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3105 - mean_squared_error: 0.2661 Epoch 358/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2996 - mean_squared_error: 0.2552 Epoch 359/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2852 - mean_squared_error: 0.2410 Epoch 360/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2946 - mean_squared_error: 0.2506 Epoch 361/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2913 - mean_squared_error: 0.2476 Epoch 362/1000 24/24 [==============================] - 0s 914us/step - loss: 0.2955 - mean_squared_error: 0.2514 Epoch 363/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3020 - mean_squared_error: 0.2580 Epoch 364/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3010 - mean_squared_error: 0.2572 Epoch 365/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3119 - mean_squared_error: 0.2681 Epoch 366/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2836 - mean_squared_error: 0.2397 Epoch 367/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2995 - mean_squared_error: 0.2555 Epoch 368/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2895 - mean_squared_error: 0.2457 Epoch 369/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2887 - mean_squared_error: 0.2449 Epoch 370/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2825 - mean_squared_error: 0.2385 Epoch 371/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3055 - mean_squared_error: 0.2616 Epoch 372/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2935 - mean_squared_error: 0.2493 Epoch 373/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2925 - mean_squared_error: 0.2486 Epoch 374/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2895 - mean_squared_error: 0.2458 Epoch 375/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3057 - mean_squared_error: 0.2618 Epoch 376/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2751 - mean_squared_error: 0.2310 Epoch 377/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2917 - mean_squared_error: 0.2478 Epoch 378/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2946 - mean_squared_error: 0.2507 Epoch 379/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3032 - mean_squared_error: 0.2593 Epoch 380/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3085 - mean_squared_error: 0.2647 Epoch 381/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2912 - mean_squared_error: 0.2474 Epoch 382/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3000 - mean_squared_error: 0.2562 Epoch 383/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3014 - mean_squared_error: 0.2578 Epoch 384/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2898 - mean_squared_error: 0.2462 Epoch 385/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3006 - mean_squared_error: 0.2571 Epoch 386/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2951 - mean_squared_error: 0.2519 Epoch 387/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3090 - mean_squared_error: 0.2661 Epoch 388/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3155 - mean_squared_error: 0.2726 Epoch 389/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2929 - mean_squared_error: 0.2500 Epoch 390/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2910 - mean_squared_error: 0.2477 Epoch 391/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3066 - mean_squared_error: 0.2632 Epoch 392/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3020 - mean_squared_error: 0.2585 Epoch 393/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2976 - mean_squared_error: 0.2544 Epoch 394/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2945 - mean_squared_error: 0.2512 Epoch 395/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2823 - mean_squared_error: 0.2389 Epoch 396/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3012 - mean_squared_error: 0.2577 Epoch 397/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2987 - mean_squared_error: 0.2554 Epoch 398/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2977 - mean_squared_error: 0.2542 Epoch 399/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2943 - mean_squared_error: 0.2508 Epoch 400/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2954 - mean_squared_error: 0.2519 Epoch 401/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3011 - mean_squared_error: 0.2576 Epoch 402/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2921 - mean_squared_error: 0.2486 Epoch 403/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2942 - mean_squared_error: 0.2506 Epoch 404/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3033 - mean_squared_error: 0.2599 Epoch 405/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2966 - mean_squared_error: 0.2534 Epoch 406/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2970 - mean_squared_error: 0.2533 Epoch 407/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3144 - mean_squared_error: 0.2709 Epoch 408/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3020 - mean_squared_error: 0.2583 Epoch 409/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2996 - mean_squared_error: 0.2563 Epoch 410/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3084 - mean_squared_error: 0.2649 Epoch 411/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3037 - mean_squared_error: 0.2604 Epoch 412/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2964 - mean_squared_error: 0.2529 Epoch 413/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2895 - mean_squared_error: 0.2458 Epoch 414/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2897 - mean_squared_error: 0.2460 Epoch 415/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2996 - mean_squared_error: 0.2558 Epoch 416/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3020 - mean_squared_error: 0.2579 Epoch 417/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2796 - mean_squared_error: 0.2353 Epoch 418/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3089 - mean_squared_error: 0.2647 Epoch 419/1000 24/24 [==============================] - 0s 914us/step - loss: 0.2969 - mean_squared_error: 0.2531 Epoch 420/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2916 - mean_squared_error: 0.2476 Epoch 421/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2927 - mean_squared_error: 0.2493 Epoch 422/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3011 - mean_squared_error: 0.2581 Epoch 423/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2917 - mean_squared_error: 0.2488 Epoch 424/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2898 - mean_squared_error: 0.2466 Epoch 425/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2957 - mean_squared_error: 0.2523 Epoch 426/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3068 - mean_squared_error: 0.2634 Epoch 427/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2954 - mean_squared_error: 0.2522 Epoch 428/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2874 - mean_squared_error: 0.2442 Epoch 429/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2941 - mean_squared_error: 0.2507 Epoch 430/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3022 - mean_squared_error: 0.2594 Epoch 431/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2912 - mean_squared_error: 0.2482 Epoch 432/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2990 - mean_squared_error: 0.2558 Epoch 433/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2987 - mean_squared_error: 0.2556 Epoch 434/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3072 - mean_squared_error: 0.2642 Epoch 435/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3046 - mean_squared_error: 0.2615 Epoch 436/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2973 - mean_squared_error: 0.2541 Epoch 437/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2970 - mean_squared_error: 0.2540 Epoch 438/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2964 - mean_squared_error: 0.2533 Epoch 439/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3085 - mean_squared_error: 0.2655 Epoch 440/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3036 - mean_squared_error: 0.2606 Epoch 441/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2953 - mean_squared_error: 0.2524 Epoch 442/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2912 - mean_squared_error: 0.2484 Epoch 443/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3041 - mean_squared_error: 0.2611 Epoch 444/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2815 - mean_squared_error: 0.2389 Epoch 445/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3106 - mean_squared_error: 0.2679 Epoch 446/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2955 - mean_squared_error: 0.2526 Epoch 447/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2921 - mean_squared_error: 0.2492 Epoch 448/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3116 - mean_squared_error: 0.2686 Epoch 449/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3062 - mean_squared_error: 0.2631 Epoch 450/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2909 - mean_squared_error: 0.2480 Epoch 451/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2988 - mean_squared_error: 0.2559 Epoch 452/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2976 - mean_squared_error: 0.2549 Epoch 453/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2853 - mean_squared_error: 0.2427 Epoch 454/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3010 - mean_squared_error: 0.2581 Epoch 455/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2953 - mean_squared_error: 0.2525 Epoch 456/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2987 - mean_squared_error: 0.2559 Epoch 457/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2932 - mean_squared_error: 0.2507 Epoch 458/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2810 - mean_squared_error: 0.2388 Epoch 459/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3156 - mean_squared_error: 0.2735 Epoch 460/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2870 - mean_squared_error: 0.2446 Epoch 461/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2988 - mean_squared_error: 0.2565 Epoch 462/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2904 - mean_squared_error: 0.2480 Epoch 463/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2954 - mean_squared_error: 0.2533 Epoch 464/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3011 - mean_squared_error: 0.2588 Epoch 465/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3031 - mean_squared_error: 0.2609 Epoch 466/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3126 - mean_squared_error: 0.2703 Epoch 467/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3051 - mean_squared_error: 0.2627 Epoch 468/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2944 - mean_squared_error: 0.2519 Epoch 469/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2873 - mean_squared_error: 0.2449 Epoch 470/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3006 - mean_squared_error: 0.2584 Epoch 471/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2990 - mean_squared_error: 0.2565 Epoch 472/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2848 - mean_squared_error: 0.2425 Epoch 473/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2981 - mean_squared_error: 0.2557 Epoch 474/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3050 - mean_squared_error: 0.2624 Epoch 475/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2978 - mean_squared_error: 0.2553 Epoch 476/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3043 - mean_squared_error: 0.2616 Epoch 477/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2994 - mean_squared_error: 0.2569 Epoch 478/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2862 - mean_squared_error: 0.2435 Epoch 479/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3035 - mean_squared_error: 0.2609 Epoch 480/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2854 - mean_squared_error: 0.2429 Epoch 481/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3012 - mean_squared_error: 0.2585 Epoch 482/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2924 - mean_squared_error: 0.2498 Epoch 483/1000 24/24 [==============================] - 0s 956us/step - loss: 0.2981 - mean_squared_error: 0.2557 Epoch 484/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2806 - mean_squared_error: 0.2381 Epoch 485/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2960 - mean_squared_error: 0.2535 Epoch 486/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3011 - mean_squared_error: 0.2589 Epoch 487/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3048 - mean_squared_error: 0.2626 Epoch 488/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2821 - mean_squared_error: 0.2400 Epoch 489/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2896 - mean_squared_error: 0.2472 Epoch 490/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3062 - mean_squared_error: 0.2638 Epoch 491/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2710 - mean_squared_error: 0.2288 Epoch 492/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2975 - mean_squared_error: 0.2551 Epoch 493/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2950 - mean_squared_error: 0.2528 Epoch 494/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2800 - mean_squared_error: 0.2376 Epoch 495/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2940 - mean_squared_error: 0.2516 Epoch 496/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2949 - mean_squared_error: 0.2525 Epoch 497/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2840 - mean_squared_error: 0.2417 Epoch 498/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2901 - mean_squared_error: 0.2477 Epoch 499/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3161 - mean_squared_error: 0.2740 Epoch 500/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2880 - mean_squared_error: 0.2460 Epoch 501/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3044 - mean_squared_error: 0.2623 Epoch 502/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2817 - mean_squared_error: 0.2394 Epoch 503/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2879 - mean_squared_error: 0.2459 Epoch 504/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3092 - mean_squared_error: 0.2669 Epoch 505/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2907 - mean_squared_error: 0.2487 Epoch 506/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2936 - mean_squared_error: 0.2518 Epoch 507/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3008 - mean_squared_error: 0.2590 Epoch 508/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3009 - mean_squared_error: 0.2590 Epoch 509/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3065 - mean_squared_error: 0.2646 Epoch 510/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2816 - mean_squared_error: 0.2398 Epoch 511/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2876 - mean_squared_error: 0.2458 Epoch 512/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2887 - mean_squared_error: 0.2470 Epoch 513/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3060 - mean_squared_error: 0.2638 Epoch 514/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2976 - mean_squared_error: 0.2559 Epoch 515/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2843 - mean_squared_error: 0.2425 Epoch 516/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2926 - mean_squared_error: 0.2506 Epoch 517/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3027 - mean_squared_error: 0.2606 Epoch 518/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2977 - mean_squared_error: 0.2561 Epoch 519/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2800 - mean_squared_error: 0.2377 Epoch 520/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3003 - mean_squared_error: 0.2586 Epoch 521/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3012 - mean_squared_error: 0.2594 Epoch 522/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2991 - mean_squared_error: 0.2576 Epoch 523/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2886 - mean_squared_error: 0.2468 Epoch 524/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3009 - mean_squared_error: 0.2594 Epoch 525/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2950 - mean_squared_error: 0.2528 Epoch 526/1000 24/24 [==============================] - 0s 707us/step - loss: 0.3125 - mean_squared_error: 0.2703 Epoch 527/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2808 - mean_squared_error: 0.2387 Epoch 528/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2947 - mean_squared_error: 0.2525 Epoch 529/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2892 - mean_squared_error: 0.2469 Epoch 530/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3124 - mean_squared_error: 0.2700 Epoch 531/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2969 - mean_squared_error: 0.2545 Epoch 532/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2952 - mean_squared_error: 0.2530 Epoch 533/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2879 - mean_squared_error: 0.2454 Epoch 534/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3147 - mean_squared_error: 0.2727 Epoch 535/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3029 - mean_squared_error: 0.2607 Epoch 536/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2743 - mean_squared_error: 0.2320 Epoch 537/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2875 - mean_squared_error: 0.2456 Epoch 538/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2947 - mean_squared_error: 0.2527 Epoch 539/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3057 - mean_squared_error: 0.2637 Epoch 540/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2995 - mean_squared_error: 0.2575 Epoch 541/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2880 - mean_squared_error: 0.2460 Epoch 542/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2893 - mean_squared_error: 0.2474 Epoch 543/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3210 - mean_squared_error: 0.2793 Epoch 544/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2879 - mean_squared_error: 0.2459 Epoch 545/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3089 - mean_squared_error: 0.2670 Epoch 546/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2981 - mean_squared_error: 0.2563 Epoch 547/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3014 - mean_squared_error: 0.2594 Epoch 548/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2814 - mean_squared_error: 0.2398 Epoch 549/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2814 - mean_squared_error: 0.2396 Epoch 550/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2925 - mean_squared_error: 0.2509 Epoch 551/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2916 - mean_squared_error: 0.2500 Epoch 552/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2936 - mean_squared_error: 0.2518 Epoch 553/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3151 - mean_squared_error: 0.2738 Epoch 554/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3109 - mean_squared_error: 0.2697 Epoch 555/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2912 - mean_squared_error: 0.2498 Epoch 556/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3018 - mean_squared_error: 0.2609 Epoch 557/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3043 - mean_squared_error: 0.2630 Epoch 558/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2997 - mean_squared_error: 0.2584 Epoch 559/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2936 - mean_squared_error: 0.2522 Epoch 560/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3006 - mean_squared_error: 0.2591 Epoch 561/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3083 - mean_squared_error: 0.2668 Epoch 562/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2968 - mean_squared_error: 0.2552 Epoch 563/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2961 - mean_squared_error: 0.2543 Epoch 564/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2851 - mean_squared_error: 0.2437 Epoch 565/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2865 - mean_squared_error: 0.2450 Epoch 566/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2830 - mean_squared_error: 0.2418 Epoch 567/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2956 - mean_squared_error: 0.2544 Epoch 568/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2728 - mean_squared_error: 0.2316 Epoch 569/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2960 - mean_squared_error: 0.2550 Epoch 570/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2986 - mean_squared_error: 0.2577 Epoch 571/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3135 - mean_squared_error: 0.2724 Epoch 572/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3035 - mean_squared_error: 0.2624 Epoch 573/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3067 - mean_squared_error: 0.2654 Epoch 574/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2939 - mean_squared_error: 0.2527 Epoch 575/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2909 - mean_squared_error: 0.2499 Epoch 576/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3089 - mean_squared_error: 0.2677 Epoch 577/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2866 - mean_squared_error: 0.2453 Epoch 578/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2879 - mean_squared_error: 0.2467 Epoch 579/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2851 - mean_squared_error: 0.2439 Epoch 580/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2840 - mean_squared_error: 0.2428 Epoch 581/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2955 - mean_squared_error: 0.2542 Epoch 582/1000 24/24 [==============================] - 0s 707us/step - loss: 0.2997 - mean_squared_error: 0.2584 Epoch 583/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3019 - mean_squared_error: 0.2604 Epoch 584/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2908 - mean_squared_error: 0.2493 Epoch 585/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2753 - mean_squared_error: 0.2343 Epoch 586/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2886 - mean_squared_error: 0.2474 Epoch 587/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2870 - mean_squared_error: 0.2457 Epoch 588/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2981 - mean_squared_error: 0.2567 Epoch 589/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2884 - mean_squared_error: 0.2467 Epoch 590/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2930 - mean_squared_error: 0.2516 Epoch 591/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3125 - mean_squared_error: 0.2713 Epoch 592/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2975 - mean_squared_error: 0.2562 Epoch 593/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3094 - mean_squared_error: 0.2682 Epoch 594/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2887 - mean_squared_error: 0.2474 Epoch 595/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3023 - mean_squared_error: 0.2609 Epoch 596/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2777 - mean_squared_error: 0.2364 Epoch 597/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2979 - mean_squared_error: 0.2565 Epoch 598/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3049 - mean_squared_error: 0.2637 Epoch 599/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3092 - mean_squared_error: 0.2678 Epoch 600/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2991 - mean_squared_error: 0.2577 Epoch 601/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2941 - mean_squared_error: 0.2526 Epoch 602/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2847 - mean_squared_error: 0.2434 Epoch 603/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2936 - mean_squared_error: 0.2521 Epoch 604/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2958 - mean_squared_error: 0.2540 Epoch 605/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2902 - mean_squared_error: 0.2487 Epoch 606/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2816 - mean_squared_error: 0.2404 Epoch 607/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2985 - mean_squared_error: 0.2573 Epoch 608/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3077 - mean_squared_error: 0.2669 Epoch 609/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2866 - mean_squared_error: 0.2457 Epoch 610/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3036 - mean_squared_error: 0.2625 Epoch 611/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2803 - mean_squared_error: 0.2392 Epoch 612/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2825 - mean_squared_error: 0.2412 Epoch 613/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2966 - mean_squared_error: 0.2555 Epoch 614/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2874 - mean_squared_error: 0.2462 Epoch 615/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2985 - mean_squared_error: 0.2573 Epoch 616/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2899 - mean_squared_error: 0.2487 Epoch 617/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2991 - mean_squared_error: 0.2578 Epoch 618/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2998 - mean_squared_error: 0.2587 Epoch 619/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2940 - mean_squared_error: 0.2524 Epoch 620/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2907 - mean_squared_error: 0.2490 Epoch 621/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2908 - mean_squared_error: 0.2495 Epoch 622/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2949 - mean_squared_error: 0.2538 Epoch 623/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3066 - mean_squared_error: 0.2655 Epoch 624/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3049 - mean_squared_error: 0.2638 Epoch 625/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2884 - mean_squared_error: 0.2474 Epoch 626/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2804 - mean_squared_error: 0.2394 Epoch 627/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2933 - mean_squared_error: 0.2522 Epoch 628/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2873 - mean_squared_error: 0.2461 Epoch 629/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2826 - mean_squared_error: 0.2415 Epoch 630/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2904 - mean_squared_error: 0.2494 Epoch 631/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2793 - mean_squared_error: 0.2385 Epoch 632/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3100 - mean_squared_error: 0.2693 Epoch 633/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2790 - mean_squared_error: 0.2384 Epoch 634/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3028 - mean_squared_error: 0.2618 Epoch 635/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3070 - mean_squared_error: 0.2662 Epoch 636/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2953 - mean_squared_error: 0.2543 Epoch 637/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2867 - mean_squared_error: 0.2456 Epoch 638/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2971 - mean_squared_error: 0.2562 Epoch 639/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2853 - mean_squared_error: 0.2442 Epoch 640/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2954 - mean_squared_error: 0.2544 Epoch 641/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2986 - mean_squared_error: 0.2576 Epoch 642/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3048 - mean_squared_error: 0.2639 Epoch 643/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3072 - mean_squared_error: 0.2661 Epoch 644/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3041 - mean_squared_error: 0.2630 Epoch 645/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3102 - mean_squared_error: 0.2692 Epoch 646/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3022 - mean_squared_error: 0.2610 Epoch 647/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3130 - mean_squared_error: 0.2722 Epoch 648/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2883 - mean_squared_error: 0.2475 Epoch 649/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2885 - mean_squared_error: 0.2476 Epoch 650/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2912 - mean_squared_error: 0.2501 Epoch 651/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2970 - mean_squared_error: 0.2558 Epoch 652/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2910 - mean_squared_error: 0.2498 Epoch 653/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2823 - mean_squared_error: 0.2412 Epoch 654/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2988 - mean_squared_error: 0.2577 Epoch 655/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3032 - mean_squared_error: 0.2623 Epoch 656/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2831 - mean_squared_error: 0.2421 Epoch 657/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2911 - mean_squared_error: 0.2501 Epoch 658/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2986 - mean_squared_error: 0.2577 Epoch 659/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2892 - mean_squared_error: 0.2483 Epoch 660/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2978 - mean_squared_error: 0.2569 Epoch 661/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2817 - mean_squared_error: 0.2410 Epoch 662/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2846 - mean_squared_error: 0.2437 Epoch 663/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3005 - mean_squared_error: 0.2596 Epoch 664/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2896 - mean_squared_error: 0.2490 Epoch 665/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2885 - mean_squared_error: 0.2477 Epoch 666/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2915 - mean_squared_error: 0.2510 Epoch 667/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3097 - mean_squared_error: 0.2690 Epoch 668/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2948 - mean_squared_error: 0.2539 Epoch 669/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2909 - mean_squared_error: 0.2500 Epoch 670/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2955 - mean_squared_error: 0.2546 Epoch 671/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2885 - mean_squared_error: 0.2475 Epoch 672/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2965 - mean_squared_error: 0.2559 Epoch 673/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2902 - mean_squared_error: 0.2495 Epoch 674/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2844 - mean_squared_error: 0.2436 Epoch 675/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2992 - mean_squared_error: 0.2583 Epoch 676/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2917 - mean_squared_error: 0.2508 Epoch 677/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3061 - mean_squared_error: 0.2650 Epoch 678/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2935 - mean_squared_error: 0.2527 Epoch 679/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2887 - mean_squared_error: 0.2478 Epoch 680/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2982 - mean_squared_error: 0.2571 Epoch 681/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3032 - mean_squared_error: 0.2625 Epoch 682/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2883 - mean_squared_error: 0.2476 Epoch 683/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3283 - mean_squared_error: 0.2875 Epoch 684/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2892 - mean_squared_error: 0.2484 Epoch 685/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2793 - mean_squared_error: 0.2385 Epoch 686/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2771 - mean_squared_error: 0.2361 Epoch 687/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2922 - mean_squared_error: 0.2513 Epoch 688/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3055 - mean_squared_error: 0.2647 Epoch 689/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2989 - mean_squared_error: 0.2580 Epoch 690/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2842 - mean_squared_error: 0.2437 Epoch 691/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2852 - mean_squared_error: 0.2444 Epoch 692/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2963 - mean_squared_error: 0.2555 Epoch 693/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2949 - mean_squared_error: 0.2544 Epoch 694/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3093 - mean_squared_error: 0.2689 Epoch 695/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3017 - mean_squared_error: 0.2613 Epoch 696/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3007 - mean_squared_error: 0.2604 Epoch 697/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2792 - mean_squared_error: 0.2388 Epoch 698/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2851 - mean_squared_error: 0.2447 Epoch 699/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2825 - mean_squared_error: 0.2422 Epoch 700/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2989 - mean_squared_error: 0.2587 Epoch 701/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2915 - mean_squared_error: 0.2514 Epoch 702/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2748 - mean_squared_error: 0.2346 Epoch 703/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2915 - mean_squared_error: 0.2515 Epoch 704/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3125 - mean_squared_error: 0.2725 Epoch 705/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3025 - mean_squared_error: 0.2626 Epoch 706/1000 24/24 [==============================] - 0s 707us/step - loss: 0.2918 - mean_squared_error: 0.2515 Epoch 707/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2848 - mean_squared_error: 0.2446 Epoch 708/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2814 - mean_squared_error: 0.2413 Epoch 709/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3055 - mean_squared_error: 0.2652 Epoch 710/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3411 - mean_squared_error: 0.3005 Epoch 711/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2812 - mean_squared_error: 0.2405 Epoch 712/1000 24/24 [==============================] - 0s 914us/step - loss: 0.2726 - mean_squared_error: 0.2317 Epoch 713/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2908 - mean_squared_error: 0.2500 Epoch 714/1000 24/24 [==============================] - 0s 914us/step - loss: 0.2925 - mean_squared_error: 0.2520 Epoch 715/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2947 - mean_squared_error: 0.2541 Epoch 716/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2854 - mean_squared_error: 0.2449 Epoch 717/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2943 - mean_squared_error: 0.2540 Epoch 718/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2962 - mean_squared_error: 0.2557 Epoch 719/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2903 - mean_squared_error: 0.2498 Epoch 720/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2772 - mean_squared_error: 0.2364 Epoch 721/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3078 - mean_squared_error: 0.2670 Epoch 722/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2877 - mean_squared_error: 0.2470 Epoch 723/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2816 - mean_squared_error: 0.2411 Epoch 724/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2864 - mean_squared_error: 0.2458 Epoch 725/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2993 - mean_squared_error: 0.2589 Epoch 726/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2776 - mean_squared_error: 0.2369 Epoch 727/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2801 - mean_squared_error: 0.2395 Epoch 728/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2723 - mean_squared_error: 0.2316 Epoch 729/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2920 - mean_squared_error: 0.2516 Epoch 730/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3093 - mean_squared_error: 0.2692 Epoch 731/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3055 - mean_squared_error: 0.2651 Epoch 732/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3033 - mean_squared_error: 0.2632 Epoch 733/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3064 - mean_squared_error: 0.2661 Epoch 734/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3014 - mean_squared_error: 0.2611 Epoch 735/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2760 - mean_squared_error: 0.2357 Epoch 736/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2929 - mean_squared_error: 0.2525 Epoch 737/1000 24/24 [==============================] - 0s 709us/step - loss: 0.2980 - mean_squared_error: 0.2575 Epoch 738/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2913 - mean_squared_error: 0.2511 Epoch 739/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2849 - mean_squared_error: 0.2448 Epoch 740/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2759 - mean_squared_error: 0.2356 Epoch 741/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2815 - mean_squared_error: 0.2412 Epoch 742/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2837 - mean_squared_error: 0.2435 Epoch 743/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2947 - mean_squared_error: 0.2545 Epoch 744/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3069 - mean_squared_error: 0.2666 Epoch 745/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2882 - mean_squared_error: 0.2475 Epoch 746/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2695 - mean_squared_error: 0.2287 Epoch 747/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2958 - mean_squared_error: 0.2553 Epoch 748/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2852 - mean_squared_error: 0.2448 Epoch 749/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2848 - mean_squared_error: 0.2445 Epoch 750/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2839 - mean_squared_error: 0.2438 Epoch 751/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2955 - mean_squared_error: 0.2556 Epoch 752/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2880 - mean_squared_error: 0.2479 Epoch 753/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3030 - mean_squared_error: 0.2629 Epoch 754/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2934 - mean_squared_error: 0.2533 Epoch 755/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2954 - mean_squared_error: 0.2554 Epoch 756/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2998 - mean_squared_error: 0.2597 Epoch 757/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2904 - mean_squared_error: 0.2503 Epoch 758/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3045 - mean_squared_error: 0.2647 Epoch 759/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2914 - mean_squared_error: 0.2512 Epoch 760/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2797 - mean_squared_error: 0.2395 Epoch 761/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2923 - mean_squared_error: 0.2524 Epoch 762/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2835 - mean_squared_error: 0.2436 Epoch 763/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2772 - mean_squared_error: 0.2374 Epoch 764/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2962 - mean_squared_error: 0.2562 Epoch 765/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3012 - mean_squared_error: 0.2611 Epoch 766/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2908 - mean_squared_error: 0.2507 Epoch 767/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2987 - mean_squared_error: 0.2586 Epoch 768/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2903 - mean_squared_error: 0.2501 Epoch 769/1000 24/24 [==============================] - 0s 914us/step - loss: 0.2873 - mean_squared_error: 0.2470 Epoch 770/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3100 - mean_squared_error: 0.2697 Epoch 771/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2957 - mean_squared_error: 0.2553 Epoch 772/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3094 - mean_squared_error: 0.2693 Epoch 773/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2991 - mean_squared_error: 0.2590 Epoch 774/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2778 - mean_squared_error: 0.2375 Epoch 775/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2870 - mean_squared_error: 0.2467 Epoch 776/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2778 - mean_squared_error: 0.2375 Epoch 777/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2916 - mean_squared_error: 0.2512 Epoch 778/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2817 - mean_squared_error: 0.2415 Epoch 779/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3022 - mean_squared_error: 0.2621 Epoch 780/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3102 - mean_squared_error: 0.2702 Epoch 781/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2832 - mean_squared_error: 0.2432 Epoch 782/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2939 - mean_squared_error: 0.2543 Epoch 783/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2868 - mean_squared_error: 0.2471 Epoch 784/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2857 - mean_squared_error: 0.2456 Epoch 785/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2748 - mean_squared_error: 0.2348 Epoch 786/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2813 - mean_squared_error: 0.2412 Epoch 787/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3027 - mean_squared_error: 0.2627 Epoch 788/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3070 - mean_squared_error: 0.2671 Epoch 789/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2931 - mean_squared_error: 0.2531 Epoch 790/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2850 - mean_squared_error: 0.2448 Epoch 791/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2863 - mean_squared_error: 0.2462 Epoch 792/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2800 - mean_squared_error: 0.2397 Epoch 793/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2893 - mean_squared_error: 0.2490 Epoch 794/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2813 - mean_squared_error: 0.2410 Epoch 795/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3070 - mean_squared_error: 0.2669 Epoch 796/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2963 - mean_squared_error: 0.2562 Epoch 797/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2929 - mean_squared_error: 0.2529 Epoch 798/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2969 - mean_squared_error: 0.2567 Epoch 799/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2880 - mean_squared_error: 0.2481 Epoch 800/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2951 - mean_squared_error: 0.2551 Epoch 801/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2772 - mean_squared_error: 0.2373 Epoch 802/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2863 - mean_squared_error: 0.2464 Epoch 803/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3048 - mean_squared_error: 0.2649 Epoch 804/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2824 - mean_squared_error: 0.2427 Epoch 805/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2749 - mean_squared_error: 0.2351 Epoch 806/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2854 - mean_squared_error: 0.2453 Epoch 807/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2948 - mean_squared_error: 0.2552 Epoch 808/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2941 - mean_squared_error: 0.2544 Epoch 809/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2926 - mean_squared_error: 0.2529 Epoch 810/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2836 - mean_squared_error: 0.2434 Epoch 811/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2997 - mean_squared_error: 0.2598 Epoch 812/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2708 - mean_squared_error: 0.2308 Epoch 813/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2859 - mean_squared_error: 0.2459 Epoch 814/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2882 - mean_squared_error: 0.2480 Epoch 815/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2821 - mean_squared_error: 0.2420 Epoch 816/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2758 - mean_squared_error: 0.2355 Epoch 817/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3132 - mean_squared_error: 0.2729 Epoch 818/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2883 - mean_squared_error: 0.2480 Epoch 819/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2967 - mean_squared_error: 0.2567 Epoch 820/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2989 - mean_squared_error: 0.2589 Epoch 821/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2882 - mean_squared_error: 0.2481 Epoch 822/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2869 - mean_squared_error: 0.2468 Epoch 823/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2779 - mean_squared_error: 0.2379 Epoch 824/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2850 - mean_squared_error: 0.2452 Epoch 825/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2864 - mean_squared_error: 0.2468 Epoch 826/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2853 - mean_squared_error: 0.2456 Epoch 827/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2902 - mean_squared_error: 0.2505 Epoch 828/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2885 - mean_squared_error: 0.2488 Epoch 829/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2776 - mean_squared_error: 0.2377 Epoch 830/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2931 - mean_squared_error: 0.2533 Epoch 831/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3057 - mean_squared_error: 0.2659 Epoch 832/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2976 - mean_squared_error: 0.2577 Epoch 833/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2940 - mean_squared_error: 0.2545 Epoch 834/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2975 - mean_squared_error: 0.2577 Epoch 835/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2825 - mean_squared_error: 0.2429 Epoch 836/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2909 - mean_squared_error: 0.2512 Epoch 837/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2897 - mean_squared_error: 0.2502 Epoch 838/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2863 - mean_squared_error: 0.2466 Epoch 839/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3016 - mean_squared_error: 0.2622 Epoch 840/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2928 - mean_squared_error: 0.2534 Epoch 841/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2928 - mean_squared_error: 0.2534 Epoch 842/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2770 - mean_squared_error: 0.2374 Epoch 843/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2932 - mean_squared_error: 0.2538 Epoch 844/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3071 - mean_squared_error: 0.2677 Epoch 845/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2821 - mean_squared_error: 0.2425 Epoch 846/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2963 - mean_squared_error: 0.2570 Epoch 847/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2805 - mean_squared_error: 0.2408 Epoch 848/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2894 - mean_squared_error: 0.2500 Epoch 849/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3045 - mean_squared_error: 0.2652 Epoch 850/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2947 - mean_squared_error: 0.2552 Epoch 851/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2813 - mean_squared_error: 0.2420 Epoch 852/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2779 - mean_squared_error: 0.2385 Epoch 853/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2813 - mean_squared_error: 0.2421 Epoch 854/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2868 - mean_squared_error: 0.2475 Epoch 855/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2912 - mean_squared_error: 0.2518 Epoch 856/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2962 - mean_squared_error: 0.2568 Epoch 857/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3010 - mean_squared_error: 0.2616 Epoch 858/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3029 - mean_squared_error: 0.2632 Epoch 859/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2928 - mean_squared_error: 0.2531 Epoch 860/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2838 - mean_squared_error: 0.2445 Epoch 861/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2908 - mean_squared_error: 0.2512 Epoch 862/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2972 - mean_squared_error: 0.2573 Epoch 863/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2923 - mean_squared_error: 0.2526 Epoch 864/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2650 - mean_squared_error: 0.2252 Epoch 865/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3011 - mean_squared_error: 0.2615 Epoch 866/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3121 - mean_squared_error: 0.2726 Epoch 867/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2941 - mean_squared_error: 0.2544 Epoch 868/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2894 - mean_squared_error: 0.2495 Epoch 869/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2989 - mean_squared_error: 0.2593 Epoch 870/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2830 - mean_squared_error: 0.2429 Epoch 871/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3043 - mean_squared_error: 0.2644 Epoch 872/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2771 - mean_squared_error: 0.2374 Epoch 873/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2939 - mean_squared_error: 0.2544 Epoch 874/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2911 - mean_squared_error: 0.2515 Epoch 875/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3101 - mean_squared_error: 0.2705 Epoch 876/1000 24/24 [==============================] - 0s 667us/step - loss: 0.2942 - mean_squared_error: 0.2547 Epoch 877/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2762 - mean_squared_error: 0.2365 Epoch 878/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2933 - mean_squared_error: 0.2540 Epoch 879/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2832 - mean_squared_error: 0.2438 Epoch 880/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2771 - mean_squared_error: 0.2377 Epoch 881/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2984 - mean_squared_error: 0.2589 Epoch 882/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2860 - mean_squared_error: 0.2466 Epoch 883/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2862 - mean_squared_error: 0.2465 Epoch 884/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2783 - mean_squared_error: 0.2390 Epoch 885/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2876 - mean_squared_error: 0.2478 Epoch 886/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2914 - mean_squared_error: 0.2521 Epoch 887/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2923 - mean_squared_error: 0.2529 Epoch 888/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2806 - mean_squared_error: 0.2410 Epoch 889/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2925 - mean_squared_error: 0.2529 Epoch 890/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2833 - mean_squared_error: 0.2438 Epoch 891/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2754 - mean_squared_error: 0.2358 Epoch 892/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2809 - mean_squared_error: 0.2414 Epoch 893/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2741 - mean_squared_error: 0.2346 Epoch 894/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2797 - mean_squared_error: 0.2403 Epoch 895/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2809 - mean_squared_error: 0.2415 Epoch 896/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2800 - mean_squared_error: 0.2405 Epoch 897/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2816 - mean_squared_error: 0.2422 Epoch 898/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2894 - mean_squared_error: 0.2500 Epoch 899/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2728 - mean_squared_error: 0.2334 Epoch 900/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2969 - mean_squared_error: 0.2575 Epoch 901/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2800 - mean_squared_error: 0.2408 Epoch 902/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2970 - mean_squared_error: 0.2579 Epoch 903/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2854 - mean_squared_error: 0.2462 Epoch 904/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2962 - mean_squared_error: 0.2571 Epoch 905/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2827 - mean_squared_error: 0.2434 Epoch 906/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2795 - mean_squared_error: 0.2404 Epoch 907/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2843 - mean_squared_error: 0.2451 Epoch 908/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2816 - mean_squared_error: 0.2424 Epoch 909/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2983 - mean_squared_error: 0.2591 Epoch 910/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2866 - mean_squared_error: 0.2473 Epoch 911/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2829 - mean_squared_error: 0.2438 Epoch 912/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2879 - mean_squared_error: 0.2488 Epoch 913/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2761 - mean_squared_error: 0.2368 Epoch 914/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2925 - mean_squared_error: 0.2531 Epoch 915/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2908 - mean_squared_error: 0.2515 Epoch 916/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2868 - mean_squared_error: 0.2478 Epoch 917/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2796 - mean_squared_error: 0.2404 Epoch 918/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2712 - mean_squared_error: 0.2319 Epoch 919/1000 24/24 [==============================] - 0s 748us/step - loss: 0.3061 - mean_squared_error: 0.2670 Epoch 920/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2905 - mean_squared_error: 0.2514 Epoch 921/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2767 - mean_squared_error: 0.2374 Epoch 922/1000 24/24 [==============================] - 0s 789us/step - loss: 0.3025 - mean_squared_error: 0.2636 Epoch 923/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2857 - mean_squared_error: 0.2470 Epoch 924/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2878 - mean_squared_error: 0.2488 Epoch 925/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2895 - mean_squared_error: 0.2500 Epoch 926/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2909 - mean_squared_error: 0.2519 Epoch 927/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2835 - mean_squared_error: 0.2443 Epoch 928/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2810 - mean_squared_error: 0.2417 Epoch 929/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3031 - mean_squared_error: 0.2642 Epoch 930/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2879 - mean_squared_error: 0.2491 Epoch 931/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2967 - mean_squared_error: 0.2577 Epoch 932/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2857 - mean_squared_error: 0.2466 Epoch 933/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2837 - mean_squared_error: 0.2447 Epoch 934/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2997 - mean_squared_error: 0.2609 Epoch 935/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2956 - mean_squared_error: 0.2567 Epoch 936/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2884 - mean_squared_error: 0.2495 Epoch 937/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2839 - mean_squared_error: 0.2453 Epoch 938/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2913 - mean_squared_error: 0.2525 Epoch 939/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2794 - mean_squared_error: 0.2408 Epoch 940/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2963 - mean_squared_error: 0.2577 Epoch 941/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2674 - mean_squared_error: 0.2285 Epoch 942/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2891 - mean_squared_error: 0.2501 Epoch 943/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2909 - mean_squared_error: 0.2519 Epoch 944/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2867 - mean_squared_error: 0.2478 Epoch 945/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2669 - mean_squared_error: 0.2280 Epoch 946/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2954 - mean_squared_error: 0.2567 Epoch 947/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2821 - mean_squared_error: 0.2431 Epoch 948/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2903 - mean_squared_error: 0.2513 Epoch 949/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2920 - mean_squared_error: 0.2528 Epoch 950/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2902 - mean_squared_error: 0.2512 Epoch 951/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2736 - mean_squared_error: 0.2344 Epoch 952/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3097 - mean_squared_error: 0.2710 Epoch 953/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2885 - mean_squared_error: 0.2496 Epoch 954/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3058 - mean_squared_error: 0.2670 Epoch 955/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3168 - mean_squared_error: 0.2779 Epoch 956/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2832 - mean_squared_error: 0.2444 Epoch 957/1000 24/24 [==============================] - 0s 873us/step - loss: 0.3103 - mean_squared_error: 0.2715 Epoch 958/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2813 - mean_squared_error: 0.2424 Epoch 959/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2876 - mean_squared_error: 0.2489 Epoch 960/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2822 - mean_squared_error: 0.2437 Epoch 961/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2903 - mean_squared_error: 0.2516 Epoch 962/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2840 - mean_squared_error: 0.2454 Epoch 963/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2940 - mean_squared_error: 0.2553 Epoch 964/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2809 - mean_squared_error: 0.2422 Epoch 965/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2904 - mean_squared_error: 0.2515 Epoch 966/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2709 - mean_squared_error: 0.2322 Epoch 967/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2909 - mean_squared_error: 0.2522 Epoch 968/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2803 - mean_squared_error: 0.2414 Epoch 969/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3005 - mean_squared_error: 0.2618 Epoch 970/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2785 - mean_squared_error: 0.2399 Epoch 971/1000 24/24 [==============================] - ETA: 0s - loss: 0.3246 - mean_squared_error: 0.28 - 0s 706us/step - loss: 0.2819 - mean_squared_error: 0.2431 Epoch 972/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2857 - mean_squared_error: 0.2468 Epoch 973/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3011 - mean_squared_error: 0.2620 Epoch 974/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2898 - mean_squared_error: 0.2509 Epoch 975/1000 24/24 [==============================] - 0s 707us/step - loss: 0.2957 - mean_squared_error: 0.2569 Epoch 976/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2926 - mean_squared_error: 0.2539 Epoch 977/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2842 - mean_squared_error: 0.2454 Epoch 978/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2886 - mean_squared_error: 0.2497 Epoch 979/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2996 - mean_squared_error: 0.2605 Epoch 980/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2719 - mean_squared_error: 0.2329 Epoch 981/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2984 - mean_squared_error: 0.2594 Epoch 982/1000 24/24 [==============================] - 0s 790us/step - loss: 0.3215 - mean_squared_error: 0.2826 Epoch 983/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2792 - mean_squared_error: 0.2406 Epoch 984/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2938 - mean_squared_error: 0.2549 Epoch 985/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2892 - mean_squared_error: 0.2505 Epoch 986/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2988 - mean_squared_error: 0.2603 Epoch 987/1000 24/24 [==============================] - 0s 831us/step - loss: 0.3057 - mean_squared_error: 0.2671 Epoch 988/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2768 - mean_squared_error: 0.2382 Epoch 989/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2881 - mean_squared_error: 0.2495 Epoch 990/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2858 - mean_squared_error: 0.2472 Epoch 991/1000 24/24 [==============================] - 0s 790us/step - loss: 0.2951 - mean_squared_error: 0.2566 Epoch 992/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2915 - mean_squared_error: 0.2531 Epoch 993/1000 24/24 [==============================] - 0s 873us/step - loss: 0.2809 - mean_squared_error: 0.2424 Epoch 994/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2879 - mean_squared_error: 0.2494 Epoch 995/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2975 - mean_squared_error: 0.2589 Epoch 996/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2829 - mean_squared_error: 0.2443 Epoch 997/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2985 - mean_squared_error: 0.2601 Epoch 998/1000 24/24 [==============================] - 0s 789us/step - loss: 0.2943 - mean_squared_error: 0.2559 Epoch 999/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2875 - mean_squared_error: 0.2487 Epoch 1000/1000 24/24 [==============================] - 0s 748us/step - loss: 0.2921 - mean_squared_error: 0.2536 Total Time Taken is : -21.97322654724121
y_pred_reg_1_test=model_reg_1_test.predict(X_valid).astype("int64")
###########################################################
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
print("The Accuracy of the model is : ",accuracy_score(y_valid,y_pred_reg_1_test))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_valid,y_pred_reg_1_test),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.53
history=history_reg_1_test.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["mean_squared_error"])
ax.set_title("Mean Squred Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'mean_squared_error'])
Reducing the number of layers and Neurons here checking it out
###################################################################
#Categorical Neural Network
###################################################################
model_cat_1=k.Sequential()
#model_cat_1.add(Flatten(input_shape=(X_train.shape[1],)))
model_cat_1.add(BatchNormalization())
model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dropout(0.2, input_shape=(60,)))
#model_cat_1.add(Dense(30,activation="relu",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.2, input_shape=(30,)))
#model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.4, input_shape=(60,)))
#model_cat_1.add(Dense(60,activation="relu",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.2, input_shape=(60,)))
#model_cat_1.add(Dense(60,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.4, input_shape=(60,)))
#model_cat_1.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.2, input_shape=(30,)))
#model_cat_1.add(Dense(30,activation="sigmoid",kernel_initializer='random_normal',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_cat_1.add(Dropout(0.2, input_shape=(30,)))
model_cat_1.add(Dense(15,activation="sigmoid",kernel_initializer="random_normal",bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_cat_1.add(Dense(9,activation="softmax"))
sgd = optimizers.SGD(lr = 0.01,momentum=0.3)
model_cat_1.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.CategoricalAccuracy())
t=time.time()
###################################################################
#
###################################################################
history_cat_1=model_cat_1.fit(X_train1,k.utils.to_categorical(y_train1),batch_size=100, epochs = 500, verbose = 1)
print("Total Time Taken is : ",t-time.time())
Epoch 1/500
WARNING:tensorflow:Layer batch_normalization_8 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
12/12 [==============================] - 0s 831us/step - loss: 0.1386 - categorical_accuracy: 0.0067
Epoch 2/500
12/12 [==============================] - 0s 831us/step - loss: 0.1383 - categorical_accuracy: 0.0067
Epoch 3/500
12/12 [==============================] - 0s 748us/step - loss: 0.1379 - categorical_accuracy: 0.0067
Epoch 4/500
12/12 [==============================] - 0s 831us/step - loss: 0.1375 - categorical_accuracy: 0.0067
Epoch 5/500
12/12 [==============================] - 0s 831us/step - loss: 0.1371 - categorical_accuracy: 0.0067
Epoch 6/500
12/12 [==============================] - 0s 914us/step - loss: 0.1367 - categorical_accuracy: 0.0067
Epoch 7/500
12/12 [==============================] - 0s 997us/step - loss: 0.1363 - categorical_accuracy: 0.0067
Epoch 8/500
12/12 [==============================] - 0s 997us/step - loss: 0.1359 - categorical_accuracy: 0.0067
Epoch 9/500
12/12 [==============================] - 0s 831us/step - loss: 0.1355 - categorical_accuracy: 0.0058
Epoch 10/500
12/12 [==============================] - 0s 748us/step - loss: 0.1352 - categorical_accuracy: 0.0067
Epoch 11/500
12/12 [==============================] - 0s 748us/step - loss: 0.1348 - categorical_accuracy: 0.0067
Epoch 12/500
12/12 [==============================] - 0s 831us/step - loss: 0.1344 - categorical_accuracy: 0.0067
Epoch 13/500
12/12 [==============================] - 0s 831us/step - loss: 0.1340 - categorical_accuracy: 0.0067
Epoch 14/500
12/12 [==============================] - 0s 831us/step - loss: 0.1337 - categorical_accuracy: 0.0067
Epoch 15/500
12/12 [==============================] - 0s 831us/step - loss: 0.1333 - categorical_accuracy: 0.0067
Epoch 16/500
12/12 [==============================] - 0s 831us/step - loss: 0.1329 - categorical_accuracy: 0.0067
Epoch 17/500
12/12 [==============================] - 0s 831us/step - loss: 0.1325 - categorical_accuracy: 0.0067
Epoch 18/500
12/12 [==============================] - 0s 748us/step - loss: 0.1322 - categorical_accuracy: 0.0067
Epoch 19/500
12/12 [==============================] - 0s 748us/step - loss: 0.1318 - categorical_accuracy: 0.0067
Epoch 20/500
12/12 [==============================] - 0s 831us/step - loss: 0.1314 - categorical_accuracy: 0.0067
Epoch 21/500
12/12 [==============================] - 0s 748us/step - loss: 0.1310 - categorical_accuracy: 0.0067
Epoch 22/500
12/12 [==============================] - 0s 665us/step - loss: 0.1307 - categorical_accuracy: 0.0067
Epoch 23/500
12/12 [==============================] - 0s 665us/step - loss: 0.1303 - categorical_accuracy: 0.0067
Epoch 24/500
12/12 [==============================] - 0s 748us/step - loss: 0.1299 - categorical_accuracy: 0.0067
Epoch 25/500
12/12 [==============================] - 0s 748us/step - loss: 0.1296 - categorical_accuracy: 0.0067
Epoch 26/500
12/12 [==============================] - 0s 748us/step - loss: 0.1292 - categorical_accuracy: 0.0083
Epoch 27/500
12/12 [==============================] - 0s 748us/step - loss: 0.1289 - categorical_accuracy: 0.0117
Epoch 28/500
12/12 [==============================] - 0s 665us/step - loss: 0.1285 - categorical_accuracy: 0.0200
Epoch 29/500
12/12 [==============================] - 0s 748us/step - loss: 0.1281 - categorical_accuracy: 0.0500
Epoch 30/500
12/12 [==============================] - 0s 665us/step - loss: 0.1278 - categorical_accuracy: 0.1034
Epoch 31/500
12/12 [==============================] - 0s 748us/step - loss: 0.1274 - categorical_accuracy: 0.1710
Epoch 32/500
12/12 [==============================] - 0s 748us/step - loss: 0.1271 - categorical_accuracy: 0.2769
Epoch 33/500
12/12 [==============================] - 0s 748us/step - loss: 0.1267 - categorical_accuracy: 0.3470
Epoch 34/500
12/12 [==============================] - 0s 665us/step - loss: 0.1264 - categorical_accuracy: 0.4012
Epoch 35/500
12/12 [==============================] - 0s 748us/step - loss: 0.1260 - categorical_accuracy: 0.4170
Epoch 36/500
12/12 [==============================] - 0s 748us/step - loss: 0.1257 - categorical_accuracy: 0.4237
Epoch 37/500
12/12 [==============================] - 0s 748us/step - loss: 0.1253 - categorical_accuracy: 0.4245
Epoch 38/500
12/12 [==============================] - 0s 665us/step - loss: 0.1250 - categorical_accuracy: 0.4262
Epoch 39/500
12/12 [==============================] - 0s 748us/step - loss: 0.1246 - categorical_accuracy: 0.4270
Epoch 40/500
12/12 [==============================] - 0s 665us/step - loss: 0.1243 - categorical_accuracy: 0.4270
Epoch 41/500
12/12 [==============================] - 0s 665us/step - loss: 0.1240 - categorical_accuracy: 0.4270
Epoch 42/500
12/12 [==============================] - 0s 831us/step - loss: 0.1236 - categorical_accuracy: 0.4270
Epoch 43/500
12/12 [==============================] - 0s 831us/step - loss: 0.1233 - categorical_accuracy: 0.4270
Epoch 44/500
12/12 [==============================] - 0s 831us/step - loss: 0.1230 - categorical_accuracy: 0.4270
Epoch 45/500
12/12 [==============================] - 0s 831us/step - loss: 0.1226 - categorical_accuracy: 0.4270
Epoch 46/500
12/12 [==============================] - 0s 831us/step - loss: 0.1223 - categorical_accuracy: 0.4270
Epoch 47/500
12/12 [==============================] - 0s 748us/step - loss: 0.1220 - categorical_accuracy: 0.4270
Epoch 48/500
12/12 [==============================] - 0s 831us/step - loss: 0.1216 - categorical_accuracy: 0.4270
Epoch 49/500
12/12 [==============================] - 0s 831us/step - loss: 0.1213 - categorical_accuracy: 0.4270
Epoch 50/500
12/12 [==============================] - 0s 831us/step - loss: 0.1210 - categorical_accuracy: 0.4270
Epoch 51/500
12/12 [==============================] - 0s 748us/step - loss: 0.1206 - categorical_accuracy: 0.4270
Epoch 52/500
12/12 [==============================] - 0s 748us/step - loss: 0.1203 - categorical_accuracy: 0.4270
Epoch 53/500
12/12 [==============================] - 0s 831us/step - loss: 0.1200 - categorical_accuracy: 0.4270
Epoch 54/500
12/12 [==============================] - 0s 914us/step - loss: 0.1197 - categorical_accuracy: 0.4270
Epoch 55/500
12/12 [==============================] - 0s 748us/step - loss: 0.1194 - categorical_accuracy: 0.4270
Epoch 56/500
12/12 [==============================] - 0s 665us/step - loss: 0.1190 - categorical_accuracy: 0.4270
Epoch 57/500
12/12 [==============================] - 0s 748us/step - loss: 0.1188 - categorical_accuracy: 0.4270
Epoch 58/500
12/12 [==============================] - 0s 748us/step - loss: 0.1184 - categorical_accuracy: 0.4270
Epoch 59/500
12/12 [==============================] - 0s 748us/step - loss: 0.1181 - categorical_accuracy: 0.4270
Epoch 60/500
12/12 [==============================] - 0s 748us/step - loss: 0.1178 - categorical_accuracy: 0.4270
Epoch 61/500
12/12 [==============================] - 0s 748us/step - loss: 0.1175 - categorical_accuracy: 0.4270
Epoch 62/500
12/12 [==============================] - 0s 748us/step - loss: 0.1172 - categorical_accuracy: 0.4270
Epoch 63/500
12/12 [==============================] - 0s 748us/step - loss: 0.1169 - categorical_accuracy: 0.4270
Epoch 64/500
12/12 [==============================] - 0s 748us/step - loss: 0.1166 - categorical_accuracy: 0.4270
Epoch 65/500
12/12 [==============================] - 0s 665us/step - loss: 0.1163 - categorical_accuracy: 0.4270
Epoch 66/500
12/12 [==============================] - 0s 748us/step - loss: 0.1160 - categorical_accuracy: 0.4270
Epoch 67/500
12/12 [==============================] - 0s 748us/step - loss: 0.1157 - categorical_accuracy: 0.4270
Epoch 68/500
12/12 [==============================] - 0s 748us/step - loss: 0.1154 - categorical_accuracy: 0.4270
Epoch 69/500
12/12 [==============================] - 0s 665us/step - loss: 0.1151 - categorical_accuracy: 0.4270
Epoch 70/500
12/12 [==============================] - 0s 748us/step - loss: 0.1149 - categorical_accuracy: 0.4270
Epoch 71/500
12/12 [==============================] - 0s 831us/step - loss: 0.1146 - categorical_accuracy: 0.4270
Epoch 72/500
12/12 [==============================] - 0s 582us/step - loss: 0.1143 - categorical_accuracy: 0.4270
Epoch 73/500
12/12 [==============================] - 0s 665us/step - loss: 0.1140 - categorical_accuracy: 0.4270
Epoch 74/500
12/12 [==============================] - 0s 665us/step - loss: 0.1137 - categorical_accuracy: 0.4270
Epoch 75/500
12/12 [==============================] - 0s 665us/step - loss: 0.1134 - categorical_accuracy: 0.4270
Epoch 76/500
12/12 [==============================] - 0s 665us/step - loss: 0.1132 - categorical_accuracy: 0.4270
Epoch 77/500
12/12 [==============================] - 0s 665us/step - loss: 0.1129 - categorical_accuracy: 0.4270
Epoch 78/500
12/12 [==============================] - 0s 665us/step - loss: 0.1126 - categorical_accuracy: 0.4270
Epoch 79/500
12/12 [==============================] - 0s 665us/step - loss: 0.1123 - categorical_accuracy: 0.4270
Epoch 80/500
12/12 [==============================] - 0s 665us/step - loss: 0.1121 - categorical_accuracy: 0.4270
Epoch 81/500
12/12 [==============================] - 0s 582us/step - loss: 0.1118 - categorical_accuracy: 0.4270
Epoch 82/500
12/12 [==============================] - 0s 665us/step - loss: 0.1116 - categorical_accuracy: 0.4270
Epoch 83/500
12/12 [==============================] - 0s 748us/step - loss: 0.1113 - categorical_accuracy: 0.4270
Epoch 84/500
12/12 [==============================] - 0s 665us/step - loss: 0.1111 - categorical_accuracy: 0.4270
Epoch 85/500
12/12 [==============================] - 0s 665us/step - loss: 0.1107 - categorical_accuracy: 0.4270
Epoch 86/500
12/12 [==============================] - 0s 665us/step - loss: 0.1105 - categorical_accuracy: 0.4270
Epoch 87/500
12/12 [==============================] - 0s 665us/step - loss: 0.1103 - categorical_accuracy: 0.4270
Epoch 88/500
12/12 [==============================] - 0s 665us/step - loss: 0.1100 - categorical_accuracy: 0.4270
Epoch 89/500
12/12 [==============================] - 0s 748us/step - loss: 0.1097 - categorical_accuracy: 0.4270
Epoch 90/500
12/12 [==============================] - 0s 665us/step - loss: 0.1095 - categorical_accuracy: 0.4270
Epoch 91/500
12/12 [==============================] - 0s 665us/step - loss: 0.1093 - categorical_accuracy: 0.4270
Epoch 92/500
12/12 [==============================] - 0s 665us/step - loss: 0.1090 - categorical_accuracy: 0.4270
Epoch 93/500
12/12 [==============================] - 0s 665us/step - loss: 0.1088 - categorical_accuracy: 0.4270
Epoch 94/500
12/12 [==============================] - 0s 665us/step - loss: 0.1085 - categorical_accuracy: 0.4270
Epoch 95/500
12/12 [==============================] - 0s 831us/step - loss: 0.1083 - categorical_accuracy: 0.4270
Epoch 96/500
12/12 [==============================] - 0s 831us/step - loss: 0.1081 - categorical_accuracy: 0.4270
Epoch 97/500
12/12 [==============================] - 0s 831us/step - loss: 0.1078 - categorical_accuracy: 0.4270
Epoch 98/500
12/12 [==============================] - 0s 748us/step - loss: 0.1076 - categorical_accuracy: 0.4270
Epoch 99/500
12/12 [==============================] - 0s 831us/step - loss: 0.1074 - categorical_accuracy: 0.4270
Epoch 100/500
12/12 [==============================] - 0s 748us/step - loss: 0.1072 - categorical_accuracy: 0.4270
Epoch 101/500
12/12 [==============================] - 0s 748us/step - loss: 0.1070 - categorical_accuracy: 0.4270
Epoch 102/500
12/12 [==============================] - 0s 748us/step - loss: 0.1067 - categorical_accuracy: 0.4270
Epoch 103/500
12/12 [==============================] - 0s 831us/step - loss: 0.1065 - categorical_accuracy: 0.4270
Epoch 104/500
12/12 [==============================] - 0s 748us/step - loss: 0.1063 - categorical_accuracy: 0.4270
Epoch 105/500
12/12 [==============================] - 0s 748us/step - loss: 0.1061 - categorical_accuracy: 0.4270
Epoch 106/500
12/12 [==============================] - 0s 748us/step - loss: 0.1059 - categorical_accuracy: 0.4270
Epoch 107/500
12/12 [==============================] - 0s 748us/step - loss: 0.1057 - categorical_accuracy: 0.4270
Epoch 108/500
12/12 [==============================] - 0s 748us/step - loss: 0.1055 - categorical_accuracy: 0.4270
Epoch 109/500
12/12 [==============================] - 0s 748us/step - loss: 0.1053 - categorical_accuracy: 0.4270
Epoch 110/500
12/12 [==============================] - 0s 831us/step - loss: 0.1050 - categorical_accuracy: 0.4270
Epoch 111/500
12/12 [==============================] - 0s 831us/step - loss: 0.1048 - categorical_accuracy: 0.4270
Epoch 112/500
12/12 [==============================] - 0s 748us/step - loss: 0.1046 - categorical_accuracy: 0.4270
Epoch 113/500
12/12 [==============================] - 0s 831us/step - loss: 0.1045 - categorical_accuracy: 0.4270
Epoch 114/500
12/12 [==============================] - 0s 831us/step - loss: 0.1042 - categorical_accuracy: 0.4270
Epoch 115/500
12/12 [==============================] - 0s 831us/step - loss: 0.1040 - categorical_accuracy: 0.4270
Epoch 116/500
12/12 [==============================] - 0s 831us/step - loss: 0.1038 - categorical_accuracy: 0.4270
Epoch 117/500
12/12 [==============================] - 0s 831us/step - loss: 0.1037 - categorical_accuracy: 0.4270
Epoch 118/500
12/12 [==============================] - ETA: 0s - loss: 0.1062 - categorical_accuracy: 0.38 - 0s 831us/step - loss: 0.1035 - categorical_accuracy: 0.4270
Epoch 119/500
12/12 [==============================] - 0s 831us/step - loss: 0.1033 - categorical_accuracy: 0.4270
Epoch 120/500
12/12 [==============================] - 0s 831us/step - loss: 0.1031 - categorical_accuracy: 0.4270
Epoch 121/500
12/12 [==============================] - 0s 748us/step - loss: 0.1029 - categorical_accuracy: 0.4270
Epoch 122/500
12/12 [==============================] - 0s 831us/step - loss: 0.1028 - categorical_accuracy: 0.4270
Epoch 123/500
12/12 [==============================] - 0s 831us/step - loss: 0.1026 - categorical_accuracy: 0.4270
Epoch 124/500
12/12 [==============================] - 0s 831us/step - loss: 0.1024 - categorical_accuracy: 0.4270
Epoch 125/500
12/12 [==============================] - 0s 665us/step - loss: 0.1022 - categorical_accuracy: 0.4270
Epoch 126/500
12/12 [==============================] - 0s 665us/step - loss: 0.1021 - categorical_accuracy: 0.4270
Epoch 127/500
12/12 [==============================] - 0s 665us/step - loss: 0.1019 - categorical_accuracy: 0.4270
Epoch 128/500
12/12 [==============================] - 0s 748us/step - loss: 0.1017 - categorical_accuracy: 0.4270
Epoch 129/500
12/12 [==============================] - 0s 748us/step - loss: 0.1015 - categorical_accuracy: 0.4270
Epoch 130/500
12/12 [==============================] - 0s 748us/step - loss: 0.1014 - categorical_accuracy: 0.4270
Epoch 131/500
12/12 [==============================] - 0s 748us/step - loss: 0.1012 - categorical_accuracy: 0.4270
Epoch 132/500
12/12 [==============================] - 0s 748us/step - loss: 0.1010 - categorical_accuracy: 0.4270
Epoch 133/500
12/12 [==============================] - 0s 665us/step - loss: 0.1009 - categorical_accuracy: 0.4270
Epoch 134/500
12/12 [==============================] - 0s 665us/step - loss: 0.1007 - categorical_accuracy: 0.4270
Epoch 135/500
12/12 [==============================] - 0s 665us/step - loss: 0.1006 - categorical_accuracy: 0.4270
Epoch 136/500
12/12 [==============================] - 0s 748us/step - loss: 0.1004 - categorical_accuracy: 0.4270
Epoch 137/500
12/12 [==============================] - 0s 665us/step - loss: 0.1002 - categorical_accuracy: 0.4270
Epoch 138/500
12/12 [==============================] - 0s 665us/step - loss: 0.1001 - categorical_accuracy: 0.4270
Epoch 139/500
12/12 [==============================] - 0s 665us/step - loss: 0.0999 - categorical_accuracy: 0.4270
Epoch 140/500
12/12 [==============================] - 0s 665us/step - loss: 0.0998 - categorical_accuracy: 0.4270
Epoch 141/500
12/12 [==============================] - 0s 665us/step - loss: 0.0996 - categorical_accuracy: 0.4270
Epoch 142/500
12/12 [==============================] - 0s 665us/step - loss: 0.0995 - categorical_accuracy: 0.4270
Epoch 143/500
12/12 [==============================] - 0s 665us/step - loss: 0.0993 - categorical_accuracy: 0.4270
Epoch 144/500
12/12 [==============================] - 0s 748us/step - loss: 0.0992 - categorical_accuracy: 0.4270
Epoch 145/500
12/12 [==============================] - 0s 665us/step - loss: 0.0990 - categorical_accuracy: 0.4270
Epoch 146/500
12/12 [==============================] - 0s 665us/step - loss: 0.0989 - categorical_accuracy: 0.4270
Epoch 147/500
12/12 [==============================] - 0s 665us/step - loss: 0.0987 - categorical_accuracy: 0.4270
Epoch 148/500
12/12 [==============================] - 0s 831us/step - loss: 0.0986 - categorical_accuracy: 0.4270
Epoch 149/500
12/12 [==============================] - 0s 748us/step - loss: 0.0984 - categorical_accuracy: 0.4270
Epoch 150/500
12/12 [==============================] - 0s 665us/step - loss: 0.0983 - categorical_accuracy: 0.4270
Epoch 151/500
12/12 [==============================] - 0s 748us/step - loss: 0.0981 - categorical_accuracy: 0.4270
Epoch 152/500
12/12 [==============================] - 0s 748us/step - loss: 0.0980 - categorical_accuracy: 0.4270
Epoch 153/500
12/12 [==============================] - 0s 748us/step - loss: 0.0978 - categorical_accuracy: 0.4270
Epoch 154/500
12/12 [==============================] - 0s 748us/step - loss: 0.0977 - categorical_accuracy: 0.4270
Epoch 155/500
12/12 [==============================] - 0s 748us/step - loss: 0.0976 - categorical_accuracy: 0.4270
Epoch 156/500
12/12 [==============================] - 0s 665us/step - loss: 0.0974 - categorical_accuracy: 0.4270
Epoch 157/500
12/12 [==============================] - 0s 748us/step - loss: 0.0973 - categorical_accuracy: 0.4270
Epoch 158/500
12/12 [==============================] - 0s 748us/step - loss: 0.0972 - categorical_accuracy: 0.4270
Epoch 159/500
12/12 [==============================] - 0s 831us/step - loss: 0.0970 - categorical_accuracy: 0.4270
Epoch 160/500
12/12 [==============================] - 0s 831us/step - loss: 0.0969 - categorical_accuracy: 0.4270
Epoch 161/500
12/12 [==============================] - 0s 831us/step - loss: 0.0968 - categorical_accuracy: 0.4270
Epoch 162/500
12/12 [==============================] - 0s 831us/step - loss: 0.0967 - categorical_accuracy: 0.4270
Epoch 163/500
12/12 [==============================] - 0s 831us/step - loss: 0.0965 - categorical_accuracy: 0.4270
Epoch 164/500
12/12 [==============================] - 0s 831us/step - loss: 0.0964 - categorical_accuracy: 0.4270
Epoch 165/500
12/12 [==============================] - 0s 831us/step - loss: 0.0963 - categorical_accuracy: 0.4270
Epoch 166/500
12/12 [==============================] - 0s 748us/step - loss: 0.0961 - categorical_accuracy: 0.4270
Epoch 167/500
12/12 [==============================] - 0s 748us/step - loss: 0.0960 - categorical_accuracy: 0.4270
Epoch 168/500
12/12 [==============================] - 0s 748us/step - loss: 0.0959 - categorical_accuracy: 0.4270
Epoch 169/500
12/12 [==============================] - 0s 748us/step - loss: 0.0957 - categorical_accuracy: 0.4270
Epoch 170/500
12/12 [==============================] - 0s 748us/step - loss: 0.0956 - categorical_accuracy: 0.4270
Epoch 171/500
12/12 [==============================] - 0s 748us/step - loss: 0.0955 - categorical_accuracy: 0.4270
Epoch 172/500
12/12 [==============================] - 0s 831us/step - loss: 0.0954 - categorical_accuracy: 0.4270
Epoch 173/500
12/12 [==============================] - 0s 914us/step - loss: 0.0952 - categorical_accuracy: 0.4270
Epoch 174/500
12/12 [==============================] - 0s 831us/step - loss: 0.0951 - categorical_accuracy: 0.4270
Epoch 175/500
12/12 [==============================] - 0s 831us/step - loss: 0.0950 - categorical_accuracy: 0.4270
Epoch 176/500
12/12 [==============================] - 0s 831us/step - loss: 0.0949 - categorical_accuracy: 0.4270
Epoch 177/500
12/12 [==============================] - 0s 748us/step - loss: 0.0947 - categorical_accuracy: 0.4270
Epoch 178/500
12/12 [==============================] - 0s 748us/step - loss: 0.0946 - categorical_accuracy: 0.4270
Epoch 179/500
12/12 [==============================] - 0s 748us/step - loss: 0.0945 - categorical_accuracy: 0.4270
Epoch 180/500
12/12 [==============================] - 0s 748us/step - loss: 0.0944 - categorical_accuracy: 0.4270
Epoch 181/500
12/12 [==============================] - 0s 665us/step - loss: 0.0943 - categorical_accuracy: 0.4270
Epoch 182/500
12/12 [==============================] - 0s 748us/step - loss: 0.0941 - categorical_accuracy: 0.4270
Epoch 183/500
12/12 [==============================] - 0s 748us/step - loss: 0.0940 - categorical_accuracy: 0.4270
Epoch 184/500
12/12 [==============================] - 0s 748us/step - loss: 0.0939 - categorical_accuracy: 0.4270
Epoch 185/500
12/12 [==============================] - 0s 748us/step - loss: 0.0938 - categorical_accuracy: 0.4270
Epoch 186/500
12/12 [==============================] - 0s 748us/step - loss: 0.0936 - categorical_accuracy: 0.4270
Epoch 187/500
12/12 [==============================] - 0s 665us/step - loss: 0.0935 - categorical_accuracy: 0.4270
Epoch 188/500
12/12 [==============================] - 0s 748us/step - loss: 0.0934 - categorical_accuracy: 0.4270
Epoch 189/500
12/12 [==============================] - 0s 748us/step - loss: 0.0933 - categorical_accuracy: 0.4270
Epoch 190/500
12/12 [==============================] - 0s 748us/step - loss: 0.0932 - categorical_accuracy: 0.4270
Epoch 191/500
12/12 [==============================] - 0s 831us/step - loss: 0.0931 - categorical_accuracy: 0.4270
Epoch 192/500
12/12 [==============================] - 0s 831us/step - loss: 0.0930 - categorical_accuracy: 0.4270
Epoch 193/500
12/12 [==============================] - 0s 831us/step - loss: 0.0928 - categorical_accuracy: 0.4270
Epoch 194/500
12/12 [==============================] - 0s 748us/step - loss: 0.0927 - categorical_accuracy: 0.4270
Epoch 195/500
12/12 [==============================] - 0s 748us/step - loss: 0.0926 - categorical_accuracy: 0.4270
Epoch 196/500
12/12 [==============================] - 0s 748us/step - loss: 0.0925 - categorical_accuracy: 0.4270
Epoch 197/500
12/12 [==============================] - 0s 665us/step - loss: 0.0924 - categorical_accuracy: 0.4270
Epoch 198/500
12/12 [==============================] - 0s 748us/step - loss: 0.0923 - categorical_accuracy: 0.4270
Epoch 199/500
12/12 [==============================] - 0s 831us/step - loss: 0.0922 - categorical_accuracy: 0.4270
Epoch 200/500
12/12 [==============================] - 0s 748us/step - loss: 0.0921 - categorical_accuracy: 0.4270
Epoch 201/500
12/12 [==============================] - 0s 831us/step - loss: 0.0919 - categorical_accuracy: 0.4270
Epoch 202/500
12/12 [==============================] - 0s 914us/step - loss: 0.0918 - categorical_accuracy: 0.4270
Epoch 203/500
12/12 [==============================] - 0s 831us/step - loss: 0.0917 - categorical_accuracy: 0.4270
Epoch 204/500
12/12 [==============================] - 0s 748us/step - loss: 0.0917 - categorical_accuracy: 0.4270
Epoch 205/500
12/12 [==============================] - 0s 831us/step - loss: 0.0915 - categorical_accuracy: 0.4270
Epoch 206/500
12/12 [==============================] - 0s 914us/step - loss: 0.0914 - categorical_accuracy: 0.4270
Epoch 207/500
12/12 [==============================] - 0s 831us/step - loss: 0.0913 - categorical_accuracy: 0.4270
Epoch 208/500
12/12 [==============================] - 0s 914us/step - loss: 0.0912 - categorical_accuracy: 0.4270
Epoch 209/500
12/12 [==============================] - 0s 748us/step - loss: 0.0911 - categorical_accuracy: 0.4270
Epoch 210/500
12/12 [==============================] - 0s 831us/step - loss: 0.0910 - categorical_accuracy: 0.4270
Epoch 211/500
12/12 [==============================] - 0s 831us/step - loss: 0.0909 - categorical_accuracy: 0.4270
Epoch 212/500
12/12 [==============================] - 0s 831us/step - loss: 0.0908 - categorical_accuracy: 0.4270
Epoch 213/500
12/12 [==============================] - 0s 748us/step - loss: 0.0907 - categorical_accuracy: 0.4270
Epoch 214/500
12/12 [==============================] - 0s 831us/step - loss: 0.0905 - categorical_accuracy: 0.4270
Epoch 215/500
12/12 [==============================] - 0s 831us/step - loss: 0.0905 - categorical_accuracy: 0.4270
Epoch 216/500
12/12 [==============================] - 0s 831us/step - loss: 0.0903 - categorical_accuracy: 0.4270
Epoch 217/500
12/12 [==============================] - 0s 831us/step - loss: 0.0902 - categorical_accuracy: 0.4270
Epoch 218/500
12/12 [==============================] - 0s 831us/step - loss: 0.0901 - categorical_accuracy: 0.4270
Epoch 219/500
12/12 [==============================] - 0s 748us/step - loss: 0.0900 - categorical_accuracy: 0.4270
Epoch 220/500
12/12 [==============================] - 0s 748us/step - loss: 0.0899 - categorical_accuracy: 0.4270
Epoch 221/500
12/12 [==============================] - 0s 831us/step - loss: 0.0898 - categorical_accuracy: 0.4270
Epoch 222/500
12/12 [==============================] - 0s 748us/step - loss: 0.0897 - categorical_accuracy: 0.4270
Epoch 223/500
12/12 [==============================] - 0s 748us/step - loss: 0.0896 - categorical_accuracy: 0.4270
Epoch 224/500
12/12 [==============================] - 0s 748us/step - loss: 0.0895 - categorical_accuracy: 0.4270
Epoch 225/500
12/12 [==============================] - 0s 831us/step - loss: 0.0894 - categorical_accuracy: 0.4270
Epoch 226/500
12/12 [==============================] - 0s 748us/step - loss: 0.0893 - categorical_accuracy: 0.4270
Epoch 227/500
12/12 [==============================] - 0s 831us/step - loss: 0.0892 - categorical_accuracy: 0.4270
Epoch 228/500
12/12 [==============================] - 0s 831us/step - loss: 0.0891 - categorical_accuracy: 0.4270
Epoch 229/500
12/12 [==============================] - 0s 748us/step - loss: 0.0890 - categorical_accuracy: 0.4270
Epoch 230/500
12/12 [==============================] - 0s 914us/step - loss: 0.0889 - categorical_accuracy: 0.4270
Epoch 231/500
12/12 [==============================] - 0s 914us/step - loss: 0.0888 - categorical_accuracy: 0.4270
Epoch 232/500
12/12 [==============================] - 0s 665us/step - loss: 0.0887 - categorical_accuracy: 0.4270
Epoch 233/500
12/12 [==============================] - 0s 665us/step - loss: 0.0886 - categorical_accuracy: 0.4270
Epoch 234/500
12/12 [==============================] - 0s 582us/step - loss: 0.0885 - categorical_accuracy: 0.4270
Epoch 235/500
12/12 [==============================] - 0s 665us/step - loss: 0.0884 - categorical_accuracy: 0.4270
Epoch 236/500
12/12 [==============================] - 0s 582us/step - loss: 0.0883 - categorical_accuracy: 0.4270
Epoch 237/500
12/12 [==============================] - 0s 665us/step - loss: 0.0882 - categorical_accuracy: 0.4270
Epoch 238/500
12/12 [==============================] - 0s 665us/step - loss: 0.0881 - categorical_accuracy: 0.4270
Epoch 239/500
12/12 [==============================] - 0s 665us/step - loss: 0.0880 - categorical_accuracy: 0.4270
Epoch 240/500
12/12 [==============================] - 0s 665us/step - loss: 0.0879 - categorical_accuracy: 0.4270
Epoch 241/500
12/12 [==============================] - 0s 665us/step - loss: 0.0878 - categorical_accuracy: 0.4270
Epoch 242/500
12/12 [==============================] - 0s 665us/step - loss: 0.0877 - categorical_accuracy: 0.4270
Epoch 243/500
12/12 [==============================] - 0s 665us/step - loss: 0.0876 - categorical_accuracy: 0.4270
Epoch 244/500
12/12 [==============================] - 0s 665us/step - loss: 0.0875 - categorical_accuracy: 0.4270
Epoch 245/500
12/12 [==============================] - 0s 665us/step - loss: 0.0874 - categorical_accuracy: 0.4270
Epoch 246/500
12/12 [==============================] - 0s 665us/step - loss: 0.0873 - categorical_accuracy: 0.4270
Epoch 247/500
12/12 [==============================] - 0s 582us/step - loss: 0.0872 - categorical_accuracy: 0.4270
Epoch 248/500
12/12 [==============================] - 0s 665us/step - loss: 0.0871 - categorical_accuracy: 0.4270
Epoch 249/500
12/12 [==============================] - 0s 665us/step - loss: 0.0870 - categorical_accuracy: 0.4270
Epoch 250/500
12/12 [==============================] - 0s 665us/step - loss: 0.0869 - categorical_accuracy: 0.4270
Epoch 251/500
12/12 [==============================] - 0s 665us/step - loss: 0.0869 - categorical_accuracy: 0.4270
Epoch 252/500
12/12 [==============================] - 0s 665us/step - loss: 0.0868 - categorical_accuracy: 0.4270
Epoch 253/500
12/12 [==============================] - 0s 665us/step - loss: 0.0867 - categorical_accuracy: 0.4270
Epoch 254/500
12/12 [==============================] - 0s 748us/step - loss: 0.0866 - categorical_accuracy: 0.4270
Epoch 255/500
12/12 [==============================] - 0s 831us/step - loss: 0.0864 - categorical_accuracy: 0.4270
Epoch 256/500
12/12 [==============================] - 0s 748us/step - loss: 0.0864 - categorical_accuracy: 0.4270
Epoch 257/500
12/12 [==============================] - 0s 748us/step - loss: 0.0863 - categorical_accuracy: 0.4270
Epoch 258/500
12/12 [==============================] - 0s 831us/step - loss: 0.0862 - categorical_accuracy: 0.4270
Epoch 259/500
12/12 [==============================] - 0s 831us/step - loss: 0.0861 - categorical_accuracy: 0.4270
Epoch 260/500
12/12 [==============================] - 0s 831us/step - loss: 0.0860 - categorical_accuracy: 0.4270
Epoch 261/500
12/12 [==============================] - 0s 748us/step - loss: 0.0859 - categorical_accuracy: 0.4270
Epoch 262/500
12/12 [==============================] - 0s 831us/step - loss: 0.0858 - categorical_accuracy: 0.4270
Epoch 263/500
12/12 [==============================] - 0s 831us/step - loss: 0.0858 - categorical_accuracy: 0.4270
Epoch 264/500
12/12 [==============================] - 0s 748us/step - loss: 0.0857 - categorical_accuracy: 0.4270
Epoch 265/500
12/12 [==============================] - 0s 831us/step - loss: 0.0856 - categorical_accuracy: 0.4270
Epoch 266/500
12/12 [==============================] - 0s 748us/step - loss: 0.0855 - categorical_accuracy: 0.4270
Epoch 267/500
12/12 [==============================] - 0s 831us/step - loss: 0.0854 - categorical_accuracy: 0.4270
Epoch 268/500
12/12 [==============================] - 0s 831us/step - loss: 0.0853 - categorical_accuracy: 0.4270
Epoch 269/500
12/12 [==============================] - 0s 748us/step - loss: 0.0852 - categorical_accuracy: 0.4270
Epoch 270/500
12/12 [==============================] - 0s 748us/step - loss: 0.0851 - categorical_accuracy: 0.4270
Epoch 271/500
12/12 [==============================] - 0s 831us/step - loss: 0.0850 - categorical_accuracy: 0.4270
Epoch 272/500
12/12 [==============================] - 0s 748us/step - loss: 0.0850 - categorical_accuracy: 0.4270
Epoch 273/500
12/12 [==============================] - 0s 831us/step - loss: 0.0849 - categorical_accuracy: 0.4270
Epoch 274/500
12/12 [==============================] - 0s 831us/step - loss: 0.0848 - categorical_accuracy: 0.4270
Epoch 275/500
12/12 [==============================] - 0s 831us/step - loss: 0.0847 - categorical_accuracy: 0.4270
Epoch 276/500
12/12 [==============================] - 0s 831us/step - loss: 0.0846 - categorical_accuracy: 0.4270
Epoch 277/500
12/12 [==============================] - 0s 748us/step - loss: 0.0846 - categorical_accuracy: 0.4270
Epoch 278/500
12/12 [==============================] - 0s 748us/step - loss: 0.0845 - categorical_accuracy: 0.4270
Epoch 279/500
12/12 [==============================] - 0s 831us/step - loss: 0.0844 - categorical_accuracy: 0.4270
Epoch 280/500
12/12 [==============================] - 0s 831us/step - loss: 0.0843 - categorical_accuracy: 0.4270
Epoch 281/500
12/12 [==============================] - 0s 831us/step - loss: 0.0842 - categorical_accuracy: 0.4270
Epoch 282/500
12/12 [==============================] - 0s 748us/step - loss: 0.0842 - categorical_accuracy: 0.4270
Epoch 283/500
12/12 [==============================] - 0s 831us/step - loss: 0.0841 - categorical_accuracy: 0.4270
Epoch 284/500
12/12 [==============================] - 0s 748us/step - loss: 0.0840 - categorical_accuracy: 0.4270
Epoch 285/500
12/12 [==============================] - 0s 748us/step - loss: 0.0839 - categorical_accuracy: 0.4270
Epoch 286/500
12/12 [==============================] - 0s 748us/step - loss: 0.0838 - categorical_accuracy: 0.4270
Epoch 287/500
12/12 [==============================] - 0s 748us/step - loss: 0.0837 - categorical_accuracy: 0.4270
Epoch 288/500
12/12 [==============================] - 0s 665us/step - loss: 0.0836 - categorical_accuracy: 0.4270
Epoch 289/500
12/12 [==============================] - 0s 665us/step - loss: 0.0836 - categorical_accuracy: 0.4270
Epoch 290/500
12/12 [==============================] - 0s 665us/step - loss: 0.0835 - categorical_accuracy: 0.4270
Epoch 291/500
12/12 [==============================] - 0s 665us/step - loss: 0.0834 - categorical_accuracy: 0.4270
Epoch 292/500
12/12 [==============================] - 0s 748us/step - loss: 0.0834 - categorical_accuracy: 0.4270
Epoch 293/500
12/12 [==============================] - 0s 665us/step - loss: 0.0833 - categorical_accuracy: 0.4270
Epoch 294/500
12/12 [==============================] - 0s 748us/step - loss: 0.0832 - categorical_accuracy: 0.4270
Epoch 295/500
12/12 [==============================] - 0s 665us/step - loss: 0.0831 - categorical_accuracy: 0.4270
Epoch 296/500
12/12 [==============================] - 0s 665us/step - loss: 0.0830 - categorical_accuracy: 0.4270
Epoch 297/500
12/12 [==============================] - 0s 748us/step - loss: 0.0830 - categorical_accuracy: 0.4270
Epoch 298/500
12/12 [==============================] - 0s 665us/step - loss: 0.0829 - categorical_accuracy: 0.4270
Epoch 299/500
12/12 [==============================] - 0s 665us/step - loss: 0.0828 - categorical_accuracy: 0.4270
Epoch 300/500
12/12 [==============================] - 0s 665us/step - loss: 0.0827 - categorical_accuracy: 0.4270
Epoch 301/500
12/12 [==============================] - 0s 665us/step - loss: 0.0827 - categorical_accuracy: 0.4270
Epoch 302/500
12/12 [==============================] - 0s 748us/step - loss: 0.0826 - categorical_accuracy: 0.4270
Epoch 303/500
12/12 [==============================] - 0s 748us/step - loss: 0.0825 - categorical_accuracy: 0.4270
Epoch 304/500
12/12 [==============================] - 0s 665us/step - loss: 0.0824 - categorical_accuracy: 0.4270
Epoch 305/500
12/12 [==============================] - 0s 665us/step - loss: 0.0824 - categorical_accuracy: 0.4270
Epoch 306/500
12/12 [==============================] - 0s 665us/step - loss: 0.0823 - categorical_accuracy: 0.4270
Epoch 307/500
12/12 [==============================] - 0s 748us/step - loss: 0.0822 - categorical_accuracy: 0.4270
Epoch 308/500
12/12 [==============================] - 0s 831us/step - loss: 0.0822 - categorical_accuracy: 0.4270
Epoch 309/500
12/12 [==============================] - 0s 748us/step - loss: 0.0821 - categorical_accuracy: 0.4270
Epoch 310/500
12/12 [==============================] - 0s 748us/step - loss: 0.0820 - categorical_accuracy: 0.4270
Epoch 311/500
12/12 [==============================] - 0s 748us/step - loss: 0.0820 - categorical_accuracy: 0.4270
Epoch 312/500
12/12 [==============================] - 0s 748us/step - loss: 0.0819 - categorical_accuracy: 0.4270
Epoch 313/500
12/12 [==============================] - 0s 831us/step - loss: 0.0818 - categorical_accuracy: 0.4270
Epoch 314/500
12/12 [==============================] - 0s 748us/step - loss: 0.0818 - categorical_accuracy: 0.4270
Epoch 315/500
12/12 [==============================] - 0s 665us/step - loss: 0.0817 - categorical_accuracy: 0.4270
Epoch 316/500
12/12 [==============================] - 0s 831us/step - loss: 0.0816 - categorical_accuracy: 0.4270
Epoch 317/500
12/12 [==============================] - 0s 831us/step - loss: 0.0816 - categorical_accuracy: 0.4270
Epoch 318/500
12/12 [==============================] - 0s 748us/step - loss: 0.0815 - categorical_accuracy: 0.4270
Epoch 319/500
12/12 [==============================] - 0s 665us/step - loss: 0.0814 - categorical_accuracy: 0.4270
Epoch 320/500
12/12 [==============================] - 0s 748us/step - loss: 0.0814 - categorical_accuracy: 0.4270
Epoch 321/500
12/12 [==============================] - 0s 748us/step - loss: 0.0813 - categorical_accuracy: 0.4270
Epoch 322/500
12/12 [==============================] - 0s 748us/step - loss: 0.0813 - categorical_accuracy: 0.4270
Epoch 323/500
12/12 [==============================] - 0s 748us/step - loss: 0.0812 - categorical_accuracy: 0.4270
Epoch 324/500
12/12 [==============================] - 0s 748us/step - loss: 0.0811 - categorical_accuracy: 0.4270
Epoch 325/500
12/12 [==============================] - 0s 748us/step - loss: 0.0810 - categorical_accuracy: 0.4270
Epoch 326/500
12/12 [==============================] - 0s 665us/step - loss: 0.0810 - categorical_accuracy: 0.4270
Epoch 327/500
12/12 [==============================] - 0s 748us/step - loss: 0.0809 - categorical_accuracy: 0.4270
Epoch 328/500
12/12 [==============================] - 0s 748us/step - loss: 0.0809 - categorical_accuracy: 0.4270
Epoch 329/500
12/12 [==============================] - 0s 748us/step - loss: 0.0808 - categorical_accuracy: 0.4270
Epoch 330/500
12/12 [==============================] - 0s 748us/step - loss: 0.0808 - categorical_accuracy: 0.4270
Epoch 331/500
12/12 [==============================] - 0s 748us/step - loss: 0.0807 - categorical_accuracy: 0.4270
Epoch 332/500
12/12 [==============================] - 0s 748us/step - loss: 0.0806 - categorical_accuracy: 0.4270
Epoch 333/500
12/12 [==============================] - 0s 748us/step - loss: 0.0806 - categorical_accuracy: 0.4270
Epoch 334/500
12/12 [==============================] - 0s 748us/step - loss: 0.0805 - categorical_accuracy: 0.4270
Epoch 335/500
12/12 [==============================] - 0s 665us/step - loss: 0.0805 - categorical_accuracy: 0.4270
Epoch 336/500
12/12 [==============================] - 0s 748us/step - loss: 0.0804 - categorical_accuracy: 0.4270
Epoch 337/500
12/12 [==============================] - 0s 748us/step - loss: 0.0803 - categorical_accuracy: 0.4270
Epoch 338/500
12/12 [==============================] - 0s 665us/step - loss: 0.0803 - categorical_accuracy: 0.4270
Epoch 339/500
12/12 [==============================] - 0s 665us/step - loss: 0.0802 - categorical_accuracy: 0.4270
Epoch 340/500
12/12 [==============================] - 0s 742us/step - loss: 0.0802 - categorical_accuracy: 0.4270
Epoch 341/500
12/12 [==============================] - 0s 665us/step - loss: 0.0801 - categorical_accuracy: 0.4270
Epoch 342/500
12/12 [==============================] - 0s 665us/step - loss: 0.0801 - categorical_accuracy: 0.4270
Epoch 343/500
12/12 [==============================] - 0s 748us/step - loss: 0.0800 - categorical_accuracy: 0.4270
Epoch 344/500
12/12 [==============================] - 0s 665us/step - loss: 0.0800 - categorical_accuracy: 0.4270
Epoch 345/500
12/12 [==============================] - 0s 748us/step - loss: 0.0799 - categorical_accuracy: 0.4270
Epoch 346/500
12/12 [==============================] - 0s 748us/step - loss: 0.0799 - categorical_accuracy: 0.4270
Epoch 347/500
12/12 [==============================] - 0s 665us/step - loss: 0.0798 - categorical_accuracy: 0.4270
Epoch 348/500
12/12 [==============================] - 0s 748us/step - loss: 0.0798 - categorical_accuracy: 0.4270
Epoch 349/500
12/12 [==============================] - 0s 748us/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 350/500
12/12 [==============================] - 0s 748us/step - loss: 0.0797 - categorical_accuracy: 0.4270
Epoch 351/500
12/12 [==============================] - 0s 665us/step - loss: 0.0796 - categorical_accuracy: 0.4270
Epoch 352/500
12/12 [==============================] - 0s 665us/step - loss: 0.0796 - categorical_accuracy: 0.4270
Epoch 353/500
12/12 [==============================] - 0s 665us/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 354/500
12/12 [==============================] - 0s 665us/step - loss: 0.0795 - categorical_accuracy: 0.4270
Epoch 355/500
12/12 [==============================] - 0s 748us/step - loss: 0.0794 - categorical_accuracy: 0.4270
Epoch 356/500
12/12 [==============================] - 0s 748us/step - loss: 0.0794 - categorical_accuracy: 0.4270
Epoch 357/500
12/12 [==============================] - 0s 665us/step - loss: 0.0794 - categorical_accuracy: 0.4270
Epoch 358/500
12/12 [==============================] - 0s 748us/step - loss: 0.0793 - categorical_accuracy: 0.4270
Epoch 359/500
12/12 [==============================] - 0s 665us/step - loss: 0.0793 - categorical_accuracy: 0.4270
Epoch 360/500
12/12 [==============================] - 0s 665us/step - loss: 0.0792 - categorical_accuracy: 0.4270
Epoch 361/500
12/12 [==============================] - 0s 914us/step - loss: 0.0792 - categorical_accuracy: 0.4270
Epoch 362/500
12/12 [==============================] - 0s 914us/step - loss: 0.0791 - categorical_accuracy: 0.4270
Epoch 363/500
12/12 [==============================] - 0s 831us/step - loss: 0.0791 - categorical_accuracy: 0.4270
Epoch 364/500
12/12 [==============================] - 0s 831us/step - loss: 0.0790 - categorical_accuracy: 0.4270
Epoch 365/500
12/12 [==============================] - ETA: 0s - loss: 0.0790 - categorical_accuracy: 0.40 - 0s 748us/step - loss: 0.0790 - categorical_accuracy: 0.4270
Epoch 366/500
12/12 [==============================] - 0s 831us/step - loss: 0.0789 - categorical_accuracy: 0.4270
Epoch 367/500
12/12 [==============================] - 0s 748us/step - loss: 0.0789 - categorical_accuracy: 0.4270
Epoch 368/500
12/12 [==============================] - 0s 748us/step - loss: 0.0788 - categorical_accuracy: 0.4270
Epoch 369/500
12/12 [==============================] - 0s 831us/step - loss: 0.0788 - categorical_accuracy: 0.4270
Epoch 370/500
12/12 [==============================] - 0s 748us/step - loss: 0.0788 - categorical_accuracy: 0.4270
Epoch 371/500
12/12 [==============================] - 0s 831us/step - loss: 0.0787 - categorical_accuracy: 0.4270
Epoch 372/500
12/12 [==============================] - 0s 748us/step - loss: 0.0787 - categorical_accuracy: 0.4270
Epoch 373/500
12/12 [==============================] - 0s 831us/step - loss: 0.0787 - categorical_accuracy: 0.4270
Epoch 374/500
12/12 [==============================] - 0s 748us/step - loss: 0.0786 - categorical_accuracy: 0.4270
Epoch 375/500
12/12 [==============================] - 0s 748us/step - loss: 0.0786 - categorical_accuracy: 0.4270
Epoch 376/500
12/12 [==============================] - 0s 748us/step - loss: 0.0785 - categorical_accuracy: 0.4270
Epoch 377/500
12/12 [==============================] - 0s 748us/step - loss: 0.0785 - categorical_accuracy: 0.4270
Epoch 378/500
12/12 [==============================] - 0s 831us/step - loss: 0.0785 - categorical_accuracy: 0.4270
Epoch 379/500
12/12 [==============================] - 0s 831us/step - loss: 0.0784 - categorical_accuracy: 0.4270
Epoch 380/500
12/12 [==============================] - 0s 748us/step - loss: 0.0784 - categorical_accuracy: 0.4270
Epoch 381/500
12/12 [==============================] - 0s 831us/step - loss: 0.0784 - categorical_accuracy: 0.4270
Epoch 382/500
12/12 [==============================] - 0s 748us/step - loss: 0.0783 - categorical_accuracy: 0.4270
Epoch 383/500
12/12 [==============================] - 0s 831us/step - loss: 0.0783 - categorical_accuracy: 0.4270
Epoch 384/500
12/12 [==============================] - 0s 748us/step - loss: 0.0782 - categorical_accuracy: 0.4270
Epoch 385/500
12/12 [==============================] - 0s 831us/step - loss: 0.0782 - categorical_accuracy: 0.4270
Epoch 386/500
12/12 [==============================] - 0s 831us/step - loss: 0.0781 - categorical_accuracy: 0.4270
Epoch 387/500
12/12 [==============================] - 0s 748us/step - loss: 0.0781 - categorical_accuracy: 0.4270
Epoch 388/500
12/12 [==============================] - 0s 748us/step - loss: 0.0781 - categorical_accuracy: 0.4270
Epoch 389/500
12/12 [==============================] - 0s 831us/step - loss: 0.0781 - categorical_accuracy: 0.4270
Epoch 390/500
12/12 [==============================] - 0s 665us/step - loss: 0.0780 - categorical_accuracy: 0.4270
Epoch 391/500
12/12 [==============================] - 0s 665us/step - loss: 0.0780 - categorical_accuracy: 0.4270
Epoch 392/500
12/12 [==============================] - 0s 665us/step - loss: 0.0780 - categorical_accuracy: 0.4270
Epoch 393/500
12/12 [==============================] - 0s 665us/step - loss: 0.0779 - categorical_accuracy: 0.4270
Epoch 394/500
12/12 [==============================] - 0s 665us/step - loss: 0.0779 - categorical_accuracy: 0.4270
Epoch 395/500
12/12 [==============================] - 0s 665us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 396/500
12/12 [==============================] - 0s 665us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 397/500
12/12 [==============================] - 0s 665us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 398/500
12/12 [==============================] - 0s 665us/step - loss: 0.0778 - categorical_accuracy: 0.4270
Epoch 399/500
12/12 [==============================] - 0s 665us/step - loss: 0.0777 - categorical_accuracy: 0.4270
Epoch 400/500
12/12 [==============================] - 0s 665us/step - loss: 0.0777 - categorical_accuracy: 0.4270
Epoch 401/500
12/12 [==============================] - 0s 665us/step - loss: 0.0777 - categorical_accuracy: 0.4270
Epoch 402/500
12/12 [==============================] - 0s 665us/step - loss: 0.0776 - categorical_accuracy: 0.4270
Epoch 403/500
12/12 [==============================] - 0s 665us/step - loss: 0.0776 - categorical_accuracy: 0.4270
Epoch 404/500
12/12 [==============================] - 0s 665us/step - loss: 0.0776 - categorical_accuracy: 0.4270
Epoch 405/500
12/12 [==============================] - 0s 665us/step - loss: 0.0775 - categorical_accuracy: 0.4270
Epoch 406/500
12/12 [==============================] - 0s 582us/step - loss: 0.0775 - categorical_accuracy: 0.4270
Epoch 407/500
12/12 [==============================] - 0s 665us/step - loss: 0.0775 - categorical_accuracy: 0.4270
Epoch 408/500
12/12 [==============================] - 0s 665us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 409/500
12/12 [==============================] - 0s 665us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 410/500
12/12 [==============================] - 0s 665us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 411/500
12/12 [==============================] - 0s 665us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 412/500
12/12 [==============================] - 0s 665us/step - loss: 0.0774 - categorical_accuracy: 0.4270
Epoch 413/500
12/12 [==============================] - 0s 665us/step - loss: 0.0773 - categorical_accuracy: 0.4270
Epoch 414/500
12/12 [==============================] - 0s 748us/step - loss: 0.0773 - categorical_accuracy: 0.4270
Epoch 415/500
12/12 [==============================] - 0s 831us/step - loss: 0.0773 - categorical_accuracy: 0.4270
Epoch 416/500
12/12 [==============================] - 0s 665us/step - loss: 0.0772 - categorical_accuracy: 0.4270
Epoch 417/500
12/12 [==============================] - 0s 748us/step - loss: 0.0772 - categorical_accuracy: 0.4270
Epoch 418/500
12/12 [==============================] - 0s 748us/step - loss: 0.0772 - categorical_accuracy: 0.4270
Epoch 419/500
12/12 [==============================] - 0s 748us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 420/500
12/12 [==============================] - 0s 748us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 421/500
12/12 [==============================] - 0s 748us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 422/500
12/12 [==============================] - 0s 748us/step - loss: 0.0771 - categorical_accuracy: 0.4270
Epoch 423/500
12/12 [==============================] - 0s 665us/step - loss: 0.0770 - categorical_accuracy: 0.4270
Epoch 424/500
12/12 [==============================] - 0s 748us/step - loss: 0.0770 - categorical_accuracy: 0.4270
Epoch 425/500
12/12 [==============================] - 0s 831us/step - loss: 0.0770 - categorical_accuracy: 0.4270
Epoch 426/500
12/12 [==============================] - 0s 831us/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 427/500
12/12 [==============================] - 0s 831us/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 428/500
12/12 [==============================] - 0s 748us/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 429/500
12/12 [==============================] - 0s 831us/step - loss: 0.0769 - categorical_accuracy: 0.4270
Epoch 430/500
12/12 [==============================] - 0s 748us/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 431/500
12/12 [==============================] - 0s 748us/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 432/500
12/12 [==============================] - 0s 831us/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 433/500
12/12 [==============================] - 0s 748us/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 434/500
12/12 [==============================] - 0s 748us/step - loss: 0.0768 - categorical_accuracy: 0.4270
Epoch 435/500
12/12 [==============================] - 0s 748us/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 436/500
12/12 [==============================] - 0s 831us/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 437/500
12/12 [==============================] - 0s 748us/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 438/500
12/12 [==============================] - 0s 748us/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 439/500
12/12 [==============================] - 0s 748us/step - loss: 0.0767 - categorical_accuracy: 0.4270
Epoch 440/500
12/12 [==============================] - 0s 748us/step - loss: 0.0766 - categorical_accuracy: 0.4270
Epoch 441/500
12/12 [==============================] - 0s 748us/step - loss: 0.0766 - categorical_accuracy: 0.4270
Epoch 442/500
12/12 [==============================] - 0s 748us/step - loss: 0.0766 - categorical_accuracy: 0.4270
Epoch 443/500
12/12 [==============================] - 0s 748us/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 444/500
12/12 [==============================] - 0s 748us/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 445/500
12/12 [==============================] - 0s 665us/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 446/500
12/12 [==============================] - 0s 665us/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 447/500
12/12 [==============================] - 0s 748us/step - loss: 0.0765 - categorical_accuracy: 0.4270
Epoch 448/500
12/12 [==============================] - 0s 665us/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 449/500
12/12 [==============================] - 0s 665us/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 450/500
12/12 [==============================] - 0s 748us/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 451/500
12/12 [==============================] - 0s 665us/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 452/500
12/12 [==============================] - 0s 665us/step - loss: 0.0764 - categorical_accuracy: 0.4270
Epoch 453/500
12/12 [==============================] - 0s 748us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 454/500
12/12 [==============================] - 0s 665us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 455/500
12/12 [==============================] - 0s 748us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 456/500
12/12 [==============================] - 0s 665us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 457/500
12/12 [==============================] - 0s 748us/step - loss: 0.0763 - categorical_accuracy: 0.4270
Epoch 458/500
12/12 [==============================] - 0s 665us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 459/500
12/12 [==============================] - 0s 748us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 460/500
12/12 [==============================] - 0s 748us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 461/500
12/12 [==============================] - 0s 748us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 462/500
12/12 [==============================] - 0s 748us/step - loss: 0.0762 - categorical_accuracy: 0.4270
Epoch 463/500
12/12 [==============================] - 0s 665us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 464/500
12/12 [==============================] - 0s 665us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 465/500
12/12 [==============================] - 0s 748us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 466/500
12/12 [==============================] - 0s 665us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 467/500
12/12 [==============================] - 0s 914us/step - loss: 0.0761 - categorical_accuracy: 0.4270
Epoch 468/500
12/12 [==============================] - 0s 831us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 469/500
12/12 [==============================] - 0s 748us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 470/500
12/12 [==============================] - 0s 665us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 471/500
12/12 [==============================] - 0s 748us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 472/500
12/12 [==============================] - 0s 831us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 473/500
12/12 [==============================] - 0s 831us/step - loss: 0.0760 - categorical_accuracy: 0.4270
Epoch 474/500
12/12 [==============================] - 0s 748us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 475/500
12/12 [==============================] - 0s 831us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 476/500
12/12 [==============================] - 0s 748us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 477/500
12/12 [==============================] - 0s 665us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 478/500
12/12 [==============================] - 0s 748us/step - loss: 0.0759 - categorical_accuracy: 0.4270
Epoch 479/500
12/12 [==============================] - 0s 831us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 480/500
12/12 [==============================] - 0s 831us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 481/500
12/12 [==============================] - 0s 831us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 482/500
12/12 [==============================] - 0s 748us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 483/500
12/12 [==============================] - 0s 831us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 484/500
12/12 [==============================] - 0s 831us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 485/500
12/12 [==============================] - 0s 748us/step - loss: 0.0758 - categorical_accuracy: 0.4270
Epoch 486/500
12/12 [==============================] - 0s 748us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 487/500
12/12 [==============================] - 0s 748us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 488/500
12/12 [==============================] - 0s 748us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 489/500
12/12 [==============================] - 0s 748us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 490/500
12/12 [==============================] - 0s 665us/step - loss: 0.0757 - categorical_accuracy: 0.4270
Epoch 491/500
12/12 [==============================] - 0s 748us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 492/500
12/12 [==============================] - 0s 748us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 493/500
12/12 [==============================] - 0s 748us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 494/500
12/12 [==============================] - 0s 748us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 495/500
12/12 [==============================] - 0s 748us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 496/500
12/12 [==============================] - 0s 665us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 497/500
12/12 [==============================] - 0s 665us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 498/500
12/12 [==============================] - 0s 665us/step - loss: 0.0756 - categorical_accuracy: 0.4270
Epoch 499/500
12/12 [==============================] - 0s 665us/step - loss: 0.0755 - categorical_accuracy: 0.4270
Epoch 500/500
12/12 [==============================] - 0s 665us/step - loss: 0.0755 - categorical_accuracy: 0.4270
Total Time Taken is : -6.582393169403076
history=history_cat_1.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["categorical_accuracy"])
ax.set_title("Categorical Accuracy")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'categorical_accuracy'])
###################################################################
#Regressional Neural Network
###################################################################
model_reg_1_test=k.Sequential()
model_reg_1_test.add(BatchNormalization(input_shape=(X_train1.shape[1],)))
model_reg_1_test.add(Flatten())
model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
#model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
#model_reg_1_test.add(Dense(30,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.5, input_shape=(50,)))
#model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.2, input_shape=(50,)))
#model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
#model_reg_1_test.add(Dropout(0.5, input_shape=(30,)))
model_reg_1_test.add(Dense(50,activation="relu",kernel_initializer='random_uniform',bias_initializer='zeros',kernel_regularizer="l2",bias_regularizer="l2"))
model_reg_1_test.add(Dense(1))
sgd = optimizers.SGD(lr = 0.01,momentum=0.6)
model_reg_1_test.compile(optimizer = sgd, loss = 'mse', metrics =k.metrics.MeanSquaredError())
###################################################################
#
###################################################################
t=time.time()
history_reg_1_test=model_reg_1_test.fit(X_train1,y_train1,batch_size=50, epochs = 1000) #add verbose later
print("Total Time Taken is : ",t-time.time())
Epoch 1/1000 24/24 [==============================] - 0s 540us/step - loss: 7.2027 - mean_squared_error: 7.1539 Epoch 2/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3760 - mean_squared_error: 0.3159 Epoch 3/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3278 - mean_squared_error: 0.2685 Epoch 4/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3348 - mean_squared_error: 0.2764 Epoch 5/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3223 - mean_squared_error: 0.2651 Epoch 6/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3265 - mean_squared_error: 0.2701 Epoch 7/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3172 - mean_squared_error: 0.2615 Epoch 8/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3195 - mean_squared_error: 0.2648 Epoch 9/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3169 - mean_squared_error: 0.2632 Epoch 10/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3039 - mean_squared_error: 0.2510 Epoch 11/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3087 - mean_squared_error: 0.2564 Epoch 12/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3099 - mean_squared_error: 0.2584 Epoch 13/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3068 - mean_squared_error: 0.2561 Epoch 14/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3002 - mean_squared_error: 0.2502 Epoch 15/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3113 - mean_squared_error: 0.2618 Epoch 16/1000 24/24 [==============================] - 0s 706us/step - loss: 0.3089 - mean_squared_error: 0.2601 Epoch 17/1000 24/24 [==============================] - 0s 665us/step - loss: 0.3010 - mean_squared_error: 0.2530 Epoch 18/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3165 - mean_squared_error: 0.2689 Epoch 19/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3076 - mean_squared_error: 0.2608 Epoch 20/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2909 - mean_squared_error: 0.2446 Epoch 21/1000 24/24 [==============================] - 0s 623us/step - loss: 0.3016 - mean_squared_error: 0.2557 Epoch 22/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2877 - mean_squared_error: 0.2426 Epoch 23/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2933 - mean_squared_error: 0.2485 Epoch 24/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2976 - mean_squared_error: 0.2533 Epoch 25/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2955 - mean_squared_error: 0.2518 Epoch 26/1000 24/24 [==============================] - 0s 540us/step - loss: 0.3026 - mean_squared_error: 0.2593 Epoch 27/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2940 - mean_squared_error: 0.2509 Epoch 28/1000 24/24 [==============================] - 0s 540us/step - loss: 0.3020 - mean_squared_error: 0.2593 Epoch 29/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2899 - mean_squared_error: 0.2480 Epoch 30/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2931 - mean_squared_error: 0.2513 Epoch 31/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2955 - mean_squared_error: 0.2541 Epoch 32/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2941 - mean_squared_error: 0.2532 Epoch 33/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2926 - mean_squared_error: 0.2520 Epoch 34/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2870 - mean_squared_error: 0.2470 Epoch 35/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2762 - mean_squared_error: 0.2362 Epoch 36/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2743 - mean_squared_error: 0.2347 Epoch 37/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2951 - mean_squared_error: 0.2560 Epoch 38/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2783 - mean_squared_error: 0.2393 Epoch 39/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2870 - mean_squared_error: 0.2484 Epoch 40/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2682 - mean_squared_error: 0.2300 Epoch 41/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2763 - mean_squared_error: 0.2385 Epoch 42/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2743 - mean_squared_error: 0.2366 Epoch 43/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2727 - mean_squared_error: 0.2351 Epoch 44/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2734 - mean_squared_error: 0.2364 Epoch 45/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2720 - mean_squared_error: 0.2349 Epoch 46/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2726 - mean_squared_error: 0.2358 Epoch 47/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2714 - mean_squared_error: 0.2351 Epoch 48/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2706 - mean_squared_error: 0.2342 Epoch 49/1000 24/24 [==============================] - 0s 631us/step - loss: 0.2620 - mean_squared_error: 0.2258 Epoch 50/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2725 - mean_squared_error: 0.2366 Epoch 51/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2910 - mean_squared_error: 0.2554 Epoch 52/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2621 - mean_squared_error: 0.2267 Epoch 53/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2755 - mean_squared_error: 0.2404 Epoch 54/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2603 - mean_squared_error: 0.2252 Epoch 55/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2708 - mean_squared_error: 0.2361 Epoch 56/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2592 - mean_squared_error: 0.2245 Epoch 57/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2598 - mean_squared_error: 0.2254 Epoch 58/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2523 - mean_squared_error: 0.2181 Epoch 59/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2609 - mean_squared_error: 0.2268 Epoch 60/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2712 - mean_squared_error: 0.2374 Epoch 61/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2622 - mean_squared_error: 0.2286 Epoch 62/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2754 - mean_squared_error: 0.2418 Epoch 63/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2596 - mean_squared_error: 0.2262 Epoch 64/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2644 - mean_squared_error: 0.2312 Epoch 65/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2592 - mean_squared_error: 0.2260 Epoch 66/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2705 - mean_squared_error: 0.2374 Epoch 67/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2523 - mean_squared_error: 0.2194 Epoch 68/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2547 - mean_squared_error: 0.2218 Epoch 69/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2591 - mean_squared_error: 0.2265 Epoch 70/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2690 - mean_squared_error: 0.2367 Epoch 71/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2642 - mean_squared_error: 0.2318 Epoch 72/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2478 - mean_squared_error: 0.2156 Epoch 73/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2586 - mean_squared_error: 0.2265 Epoch 74/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2476 - mean_squared_error: 0.2157 Epoch 75/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2497 - mean_squared_error: 0.2179 Epoch 76/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2547 - mean_squared_error: 0.2229 Epoch 77/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2574 - mean_squared_error: 0.2258 Epoch 78/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2557 - mean_squared_error: 0.2243 Epoch 79/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2647 - mean_squared_error: 0.2336 Epoch 80/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2576 - mean_squared_error: 0.2263 Epoch 81/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2479 - mean_squared_error: 0.2169 Epoch 82/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2432 - mean_squared_error: 0.2123 Epoch 83/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2543 - mean_squared_error: 0.2233 Epoch 84/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2500 - mean_squared_error: 0.2192 Epoch 85/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2479 - mean_squared_error: 0.2170 Epoch 86/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2489 - mean_squared_error: 0.2183 Epoch 87/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2554 - mean_squared_error: 0.2248 Epoch 88/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2572 - mean_squared_error: 0.2267 Epoch 89/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2547 - mean_squared_error: 0.2244 Epoch 90/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2639 - mean_squared_error: 0.2337 Epoch 91/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2417 - mean_squared_error: 0.2115 Epoch 92/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2472 - mean_squared_error: 0.2172 Epoch 93/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2544 - mean_squared_error: 0.2244 Epoch 94/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2449 - mean_squared_error: 0.2149 Epoch 95/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2526 - mean_squared_error: 0.2227 Epoch 96/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2459 - mean_squared_error: 0.2161 Epoch 97/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2450 - mean_squared_error: 0.2153 Epoch 98/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2510 - mean_squared_error: 0.2213 Epoch 99/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2494 - mean_squared_error: 0.2199 Epoch 100/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2505 - mean_squared_error: 0.2212 Epoch 101/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2443 - mean_squared_error: 0.2148 Epoch 102/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2482 - mean_squared_error: 0.2189 Epoch 103/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2424 - mean_squared_error: 0.2130 Epoch 104/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2512 - mean_squared_error: 0.2219 Epoch 105/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2646 - mean_squared_error: 0.2356 Epoch 106/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2649 - mean_squared_error: 0.2361 Epoch 107/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2440 - mean_squared_error: 0.2152 Epoch 108/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2443 - mean_squared_error: 0.2154 Epoch 109/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2498 - mean_squared_error: 0.2210 Epoch 110/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2469 - mean_squared_error: 0.2180 Epoch 111/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2413 - mean_squared_error: 0.2126 Epoch 112/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2407 - mean_squared_error: 0.2120 Epoch 113/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2359 - mean_squared_error: 0.2072 Epoch 114/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2472 - mean_squared_error: 0.2186 Epoch 115/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2449 - mean_squared_error: 0.2163 Epoch 116/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2569 - mean_squared_error: 0.2284 Epoch 117/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2546 - mean_squared_error: 0.2260 Epoch 118/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2291 - mean_squared_error: 0.2006 Epoch 119/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2485 - mean_squared_error: 0.2200 Epoch 120/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2333 - mean_squared_error: 0.2049 Epoch 121/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2426 - mean_squared_error: 0.2142 Epoch 122/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2457 - mean_squared_error: 0.2173 Epoch 123/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2431 - mean_squared_error: 0.2150 Epoch 124/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2477 - mean_squared_error: 0.2195 Epoch 125/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2411 - mean_squared_error: 0.2128 Epoch 126/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2478 - mean_squared_error: 0.2196 Epoch 127/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2351 - mean_squared_error: 0.2069 Epoch 128/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2301 - mean_squared_error: 0.2019 Epoch 129/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2379 - mean_squared_error: 0.2096 Epoch 130/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2447 - mean_squared_error: 0.2166 Epoch 131/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2435 - mean_squared_error: 0.2154 Epoch 132/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2514 - mean_squared_error: 0.2233 Epoch 133/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2468 - mean_squared_error: 0.2187 Epoch 134/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2374 - mean_squared_error: 0.2095 Epoch 135/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2430 - mean_squared_error: 0.2152 Epoch 136/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2486 - mean_squared_error: 0.2208 Epoch 137/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2432 - mean_squared_error: 0.2154 Epoch 138/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2291 - mean_squared_error: 0.2014 Epoch 139/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2329 - mean_squared_error: 0.2051 Epoch 140/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2528 - mean_squared_error: 0.2252 Epoch 141/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2486 - mean_squared_error: 0.2209 Epoch 142/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2292 - mean_squared_error: 0.2018 Epoch 143/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2423 - mean_squared_error: 0.2148 Epoch 144/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2627 - mean_squared_error: 0.2354 Epoch 145/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2380 - mean_squared_error: 0.2106 Epoch 146/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2419 - mean_squared_error: 0.2144 Epoch 147/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2480 - mean_squared_error: 0.2207 Epoch 148/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2266 - mean_squared_error: 0.1994 Epoch 149/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2338 - mean_squared_error: 0.2065 Epoch 150/1000 24/24 [==============================] - ETA: 0s - loss: 0.3088 - mean_squared_error: 0.28 - 0s 665us/step - loss: 0.2522 - mean_squared_error: 0.2251 Epoch 151/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2413 - mean_squared_error: 0.2142 Epoch 152/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2314 - mean_squared_error: 0.2044 Epoch 153/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2325 - mean_squared_error: 0.2055 Epoch 154/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2499 - mean_squared_error: 0.2228 Epoch 155/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2387 - mean_squared_error: 0.2118 Epoch 156/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2359 - mean_squared_error: 0.2091 Epoch 157/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2316 - mean_squared_error: 0.2047 Epoch 158/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2449 - mean_squared_error: 0.2179 Epoch 159/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2368 - mean_squared_error: 0.2102 Epoch 160/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2407 - mean_squared_error: 0.2141 Epoch 161/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2464 - mean_squared_error: 0.2199 Epoch 162/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2509 - mean_squared_error: 0.2244 Epoch 163/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2401 - mean_squared_error: 0.2138 Epoch 164/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2531 - mean_squared_error: 0.2270 Epoch 165/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2425 - mean_squared_error: 0.2164 Epoch 166/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2255 - mean_squared_error: 0.1991 Epoch 167/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2314 - mean_squared_error: 0.2051 Epoch 168/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2319 - mean_squared_error: 0.2055 Epoch 169/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2421 - mean_squared_error: 0.2159 Epoch 170/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2282 - mean_squared_error: 0.2021 Epoch 171/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2455 - mean_squared_error: 0.2195 Epoch 172/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2357 - mean_squared_error: 0.2095 Epoch 173/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2366 - mean_squared_error: 0.2106 Epoch 174/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2481 - mean_squared_error: 0.2220 Epoch 175/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2305 - mean_squared_error: 0.2046 Epoch 176/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2402 - mean_squared_error: 0.2141 Epoch 177/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2417 - mean_squared_error: 0.2158 Epoch 178/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2316 - mean_squared_error: 0.2057 Epoch 179/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2490 - mean_squared_error: 0.2231 Epoch 180/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2387 - mean_squared_error: 0.2128 Epoch 181/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2382 - mean_squared_error: 0.2123 Epoch 182/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2333 - mean_squared_error: 0.2074 Epoch 183/1000 24/24 [==============================] - 0s 473us/step - loss: 0.2397 - mean_squared_error: 0.2139 Epoch 184/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2497 - mean_squared_error: 0.2242 Epoch 185/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2407 - mean_squared_error: 0.2150 Epoch 186/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2332 - mean_squared_error: 0.2077 Epoch 187/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2333 - mean_squared_error: 0.2077 Epoch 188/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2308 - mean_squared_error: 0.2054 Epoch 189/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2558 - mean_squared_error: 0.2304 Epoch 190/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2306 - mean_squared_error: 0.2052 Epoch 191/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2319 - mean_squared_error: 0.2066 Epoch 192/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2376 - mean_squared_error: 0.2124 Epoch 193/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2406 - mean_squared_error: 0.2156 Epoch 194/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2339 - mean_squared_error: 0.2088 Epoch 195/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2370 - mean_squared_error: 0.2121 Epoch 196/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2412 - mean_squared_error: 0.2164 Epoch 197/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2376 - mean_squared_error: 0.2129 Epoch 198/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2289 - mean_squared_error: 0.2041 Epoch 199/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2372 - mean_squared_error: 0.2125 Epoch 200/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2300 - mean_squared_error: 0.2053 Epoch 201/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2406 - mean_squared_error: 0.2159 Epoch 202/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2402 - mean_squared_error: 0.2155 Epoch 203/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2419 - mean_squared_error: 0.2173 Epoch 204/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2457 - mean_squared_error: 0.2214 Epoch 205/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2474 - mean_squared_error: 0.2230 Epoch 206/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2488 - mean_squared_error: 0.2245 Epoch 207/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2438 - mean_squared_error: 0.2197 Epoch 208/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2472 - mean_squared_error: 0.2230 Epoch 209/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2397 - mean_squared_error: 0.2154 Epoch 210/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2314 - mean_squared_error: 0.2073 Epoch 211/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2391 - mean_squared_error: 0.2150 Epoch 212/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2414 - mean_squared_error: 0.2174 Epoch 213/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2269 - mean_squared_error: 0.2029 Epoch 214/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2351 - mean_squared_error: 0.2111 Epoch 215/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2295 - mean_squared_error: 0.2055 Epoch 216/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2327 - mean_squared_error: 0.2086 Epoch 217/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2331 - mean_squared_error: 0.2091 Epoch 218/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2375 - mean_squared_error: 0.2135 Epoch 219/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2216 - mean_squared_error: 0.1977 Epoch 220/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2376 - mean_squared_error: 0.2137 Epoch 221/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2565 - mean_squared_error: 0.2327 Epoch 222/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2311 - mean_squared_error: 0.2074 Epoch 223/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2309 - mean_squared_error: 0.2072 Epoch 224/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2303 - mean_squared_error: 0.2066 Epoch 225/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2152 Epoch 226/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2377 - mean_squared_error: 0.2141 Epoch 227/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2205 - mean_squared_error: 0.1970 Epoch 228/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2444 - mean_squared_error: 0.2210 Epoch 229/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2286 - mean_squared_error: 0.2052 Epoch 230/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2251 - mean_squared_error: 0.2016 Epoch 231/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2265 - mean_squared_error: 0.2030 Epoch 232/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2441 - mean_squared_error: 0.2206 Epoch 233/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2490 - mean_squared_error: 0.2256 Epoch 234/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2377 - mean_squared_error: 0.2142 Epoch 235/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2381 - mean_squared_error: 0.2148 Epoch 236/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2356 - mean_squared_error: 0.2122 Epoch 237/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2375 - mean_squared_error: 0.2141 Epoch 238/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2397 - mean_squared_error: 0.2164 Epoch 239/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2305 - mean_squared_error: 0.2072 Epoch 240/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2267 - mean_squared_error: 0.2035 Epoch 241/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2276 - mean_squared_error: 0.2045 Epoch 242/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2323 - mean_squared_error: 0.2091 Epoch 243/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2315 - mean_squared_error: 0.2084 Epoch 244/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2354 - mean_squared_error: 0.2123 Epoch 245/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2386 - mean_squared_error: 0.2157 Epoch 246/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2349 - mean_squared_error: 0.2119 Epoch 247/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2278 - mean_squared_error: 0.2049 Epoch 248/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2334 - mean_squared_error: 0.2105 Epoch 249/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2401 - mean_squared_error: 0.2170 Epoch 250/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2352 - mean_squared_error: 0.2123 Epoch 251/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2271 - mean_squared_error: 0.2043 Epoch 252/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2305 - mean_squared_error: 0.2077 Epoch 253/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2246 - mean_squared_error: 0.2018 Epoch 254/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2314 - mean_squared_error: 0.2087 Epoch 255/1000 24/24 [==============================] - ETA: 0s - loss: 0.2132 - mean_squared_error: 0.19 - 0s 540us/step - loss: 0.2465 - mean_squared_error: 0.2236 Epoch 256/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2284 - mean_squared_error: 0.2057 Epoch 257/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2363 - mean_squared_error: 0.2136 Epoch 258/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2290 - mean_squared_error: 0.2063 Epoch 259/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2316 - mean_squared_error: 0.2088 Epoch 260/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2373 - mean_squared_error: 0.2146 Epoch 261/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2342 - mean_squared_error: 0.2115 Epoch 262/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2610 - mean_squared_error: 0.2385 Epoch 263/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2377 - mean_squared_error: 0.2152 Epoch 264/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2233 - mean_squared_error: 0.2009 Epoch 265/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2429 - mean_squared_error: 0.2204 Epoch 266/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2280 - mean_squared_error: 0.2056 Epoch 267/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2365 - mean_squared_error: 0.2141 Epoch 268/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2350 - mean_squared_error: 0.2127 Epoch 269/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2325 - mean_squared_error: 0.2101 Epoch 270/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2243 - mean_squared_error: 0.2020 Epoch 271/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2222 - mean_squared_error: 0.1997 Epoch 272/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2215 - mean_squared_error: 0.1991 Epoch 273/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2403 - mean_squared_error: 0.2179 Epoch 274/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2340 - mean_squared_error: 0.2116 Epoch 275/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2581 - mean_squared_error: 0.2361 Epoch 276/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2456 - mean_squared_error: 0.2236 Epoch 277/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2264 - mean_squared_error: 0.2042 Epoch 278/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2277 - mean_squared_error: 0.2055 Epoch 279/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2394 - mean_squared_error: 0.2174 Epoch 280/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2218 - mean_squared_error: 0.1999 Epoch 281/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2435 - mean_squared_error: 0.2217 Epoch 282/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2510 - mean_squared_error: 0.2293 Epoch 283/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2431 - mean_squared_error: 0.2213 Epoch 284/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2181 - mean_squared_error: 0.1964 Epoch 285/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2261 - mean_squared_error: 0.2042 Epoch 286/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2351 - mean_squared_error: 0.2134 Epoch 287/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2417 - mean_squared_error: 0.2200 Epoch 288/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2323 - mean_squared_error: 0.2106 Epoch 289/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2315 - mean_squared_error: 0.2099 Epoch 290/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2359 - mean_squared_error: 0.2142 Epoch 291/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2431 - mean_squared_error: 0.2216 Epoch 292/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2220 - mean_squared_error: 0.2004 Epoch 293/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2436 - mean_squared_error: 0.2218 Epoch 294/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2329 - mean_squared_error: 0.2112 Epoch 295/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2262 - mean_squared_error: 0.2045 Epoch 296/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2288 - mean_squared_error: 0.2071 Epoch 297/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2311 - mean_squared_error: 0.2096 Epoch 298/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2324 - mean_squared_error: 0.2109 Epoch 299/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2497 - mean_squared_error: 0.2282 Epoch 300/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2379 - mean_squared_error: 0.2164 Epoch 301/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2405 - mean_squared_error: 0.2191 Epoch 302/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2276 - mean_squared_error: 0.2061 Epoch 303/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2409 - mean_squared_error: 0.2194 Epoch 304/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2348 - mean_squared_error: 0.2134 Epoch 305/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2411 - mean_squared_error: 0.2199 Epoch 306/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2302 - mean_squared_error: 0.2090 Epoch 307/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2204 - mean_squared_error: 0.1991 Epoch 308/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2346 - mean_squared_error: 0.2134 Epoch 309/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2407 - mean_squared_error: 0.2195 Epoch 310/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2286 - mean_squared_error: 0.2074 Epoch 311/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2489 - mean_squared_error: 0.2278 Epoch 312/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2292 - mean_squared_error: 0.2081 Epoch 313/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2320 - mean_squared_error: 0.2110 Epoch 314/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2203 - mean_squared_error: 0.1992 Epoch 315/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2380 - mean_squared_error: 0.2169 Epoch 316/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2361 - mean_squared_error: 0.2150 Epoch 317/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2373 - mean_squared_error: 0.2163 Epoch 318/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2301 - mean_squared_error: 0.2091 Epoch 319/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2189 - mean_squared_error: 0.1979 Epoch 320/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2268 - mean_squared_error: 0.2057 Epoch 321/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2258 - mean_squared_error: 0.2047 Epoch 322/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2203 - mean_squared_error: 0.1994 Epoch 323/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2266 - mean_squared_error: 0.2056 Epoch 324/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2204 - mean_squared_error: 0.1994 Epoch 325/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2310 - mean_squared_error: 0.2101 Epoch 326/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2180 Epoch 327/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2378 - mean_squared_error: 0.2169 Epoch 328/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2233 - mean_squared_error: 0.2025 Epoch 329/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2251 - mean_squared_error: 0.2042 Epoch 330/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2372 - mean_squared_error: 0.2163 Epoch 331/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2211 - mean_squared_error: 0.2001 Epoch 332/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2261 - mean_squared_error: 0.2052 Epoch 333/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2526 - mean_squared_error: 0.2319 Epoch 334/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2206 - mean_squared_error: 0.1997 Epoch 335/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2387 - mean_squared_error: 0.2179 Epoch 336/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2226 - mean_squared_error: 0.2020 Epoch 337/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2248 - mean_squared_error: 0.2041 Epoch 338/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2385 - mean_squared_error: 0.2178 Epoch 339/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2370 - mean_squared_error: 0.2164 Epoch 340/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2275 - mean_squared_error: 0.2068 Epoch 341/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2301 - mean_squared_error: 0.2095 Epoch 342/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2254 - mean_squared_error: 0.2048 Epoch 343/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2362 - mean_squared_error: 0.2155 Epoch 344/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2337 - mean_squared_error: 0.2131 Epoch 345/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2398 - mean_squared_error: 0.2194 Epoch 346/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2395 - mean_squared_error: 0.2189 Epoch 347/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2308 - mean_squared_error: 0.2102 Epoch 348/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2463 - mean_squared_error: 0.2256 Epoch 349/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2266 - mean_squared_error: 0.2061 Epoch 350/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2242 - mean_squared_error: 0.2037 Epoch 351/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2505 - mean_squared_error: 0.2298 Epoch 352/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2419 - mean_squared_error: 0.2215 Epoch 353/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2283 - mean_squared_error: 0.2078 Epoch 354/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2456 - mean_squared_error: 0.2252 Epoch 355/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2573 - mean_squared_error: 0.2370 Epoch 356/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2352 - mean_squared_error: 0.2149 Epoch 357/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2321 - mean_squared_error: 0.2117 Epoch 358/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2371 - mean_squared_error: 0.2167 Epoch 359/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2331 - mean_squared_error: 0.2128 Epoch 360/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2414 - mean_squared_error: 0.2211 Epoch 361/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2275 - mean_squared_error: 0.2071 Epoch 362/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2282 - mean_squared_error: 0.2079 Epoch 363/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2196 - mean_squared_error: 0.1994 Epoch 364/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2316 - mean_squared_error: 0.2113 Epoch 365/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2240 - mean_squared_error: 0.2039 Epoch 366/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2294 - mean_squared_error: 0.2092 Epoch 367/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2301 - mean_squared_error: 0.2099 Epoch 368/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2237 - mean_squared_error: 0.2036 Epoch 369/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2282 - mean_squared_error: 0.2080 Epoch 370/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2286 - mean_squared_error: 0.2084 Epoch 371/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2499 - mean_squared_error: 0.2297 Epoch 372/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2211 - mean_squared_error: 0.2009 Epoch 373/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2459 - mean_squared_error: 0.2258 Epoch 374/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2401 - mean_squared_error: 0.2200 Epoch 375/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2376 - mean_squared_error: 0.2177 Epoch 376/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2336 - mean_squared_error: 0.2135 Epoch 377/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2277 - mean_squared_error: 0.2077 Epoch 378/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2343 - mean_squared_error: 0.2141 Epoch 379/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2402 - mean_squared_error: 0.2202 Epoch 380/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2338 - mean_squared_error: 0.2138 Epoch 381/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2251 - mean_squared_error: 0.2050 Epoch 382/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2466 - mean_squared_error: 0.2266 Epoch 383/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2259 - mean_squared_error: 0.2059 Epoch 384/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2238 - mean_squared_error: 0.2039 Epoch 385/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2377 - mean_squared_error: 0.2179 Epoch 386/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2398 - mean_squared_error: 0.2202 Epoch 387/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2391 - mean_squared_error: 0.2194 Epoch 388/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2281 - mean_squared_error: 0.2083 Epoch 389/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2372 - mean_squared_error: 0.2174 Epoch 390/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2268 - mean_squared_error: 0.2070 Epoch 391/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2272 - mean_squared_error: 0.2076 Epoch 392/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2397 - mean_squared_error: 0.2200 Epoch 393/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2311 - mean_squared_error: 0.2113 Epoch 394/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2273 - mean_squared_error: 0.2075 Epoch 395/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2222 - mean_squared_error: 0.2025 Epoch 396/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2475 - mean_squared_error: 0.2278 Epoch 397/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2399 - mean_squared_error: 0.2204 Epoch 398/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2257 - mean_squared_error: 0.2060 Epoch 399/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2287 - mean_squared_error: 0.2090 Epoch 400/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2349 - mean_squared_error: 0.2151 Epoch 401/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2383 - mean_squared_error: 0.2187 Epoch 402/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2374 - mean_squared_error: 0.2175 Epoch 403/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2330 - mean_squared_error: 0.2131 Epoch 404/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2257 - mean_squared_error: 0.2059 Epoch 405/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2519 - mean_squared_error: 0.2322 Epoch 406/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2316 - mean_squared_error: 0.2118 Epoch 407/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2364 - mean_squared_error: 0.2168 Epoch 408/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2234 - mean_squared_error: 0.2036 Epoch 409/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2223 - mean_squared_error: 0.2026 Epoch 410/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2197 - mean_squared_error: 0.2002 Epoch 411/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2329 - mean_squared_error: 0.2133 Epoch 412/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2254 - mean_squared_error: 0.2056 Epoch 413/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2337 - mean_squared_error: 0.2139 Epoch 414/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2359 - mean_squared_error: 0.2161 Epoch 415/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2264 - mean_squared_error: 0.2068 Epoch 416/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2255 - mean_squared_error: 0.2059 Epoch 417/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2219 - mean_squared_error: 0.2023 Epoch 418/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2221 - mean_squared_error: 0.2025 Epoch 419/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2319 - mean_squared_error: 0.2123 Epoch 420/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2291 - mean_squared_error: 0.2095 Epoch 421/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2259 - mean_squared_error: 0.2063 Epoch 422/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2192 - mean_squared_error: 0.1998 Epoch 423/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2222 - mean_squared_error: 0.2026 Epoch 424/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2133 - mean_squared_error: 0.1938 Epoch 425/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2311 - mean_squared_error: 0.2115 Epoch 426/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2344 - mean_squared_error: 0.2149 Epoch 427/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2372 - mean_squared_error: 0.2177 Epoch 428/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2368 - mean_squared_error: 0.2173 Epoch 429/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2367 - mean_squared_error: 0.2173 Epoch 430/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2281 - mean_squared_error: 0.2086 Epoch 431/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2236 - mean_squared_error: 0.2042 Epoch 432/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2255 - mean_squared_error: 0.2060 Epoch 433/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2190 - mean_squared_error: 0.1996 Epoch 434/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2272 - mean_squared_error: 0.2077 Epoch 435/1000 24/24 [==============================] - ETA: 0s - loss: 0.2856 - mean_squared_error: 0.26 - 0s 540us/step - loss: 0.2269 - mean_squared_error: 0.2074 Epoch 436/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2373 - mean_squared_error: 0.2179 Epoch 437/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2348 - mean_squared_error: 0.2154 Epoch 438/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2396 - mean_squared_error: 0.2201 Epoch 439/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2135 - mean_squared_error: 0.1942 Epoch 440/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2509 - mean_squared_error: 0.2316 Epoch 441/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2343 - mean_squared_error: 0.2150 Epoch 442/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2340 - mean_squared_error: 0.2146 Epoch 443/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2282 - mean_squared_error: 0.2089 Epoch 444/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2356 - mean_squared_error: 0.2161 Epoch 445/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2294 - mean_squared_error: 0.2100 Epoch 446/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2335 - mean_squared_error: 0.2141 Epoch 447/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2404 - mean_squared_error: 0.2210 Epoch 448/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2229 - mean_squared_error: 0.2035 Epoch 449/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2287 - mean_squared_error: 0.2093 Epoch 450/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2294 - mean_squared_error: 0.2100 Epoch 451/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2327 - mean_squared_error: 0.2133 Epoch 452/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2342 - mean_squared_error: 0.2148 Epoch 453/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2167 - mean_squared_error: 0.1973 Epoch 454/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2410 - mean_squared_error: 0.2217 Epoch 455/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2307 - mean_squared_error: 0.2115 Epoch 456/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2250 - mean_squared_error: 0.2058 Epoch 457/1000 24/24 [==============================] - 0s 692us/step - loss: 0.2353 - mean_squared_error: 0.2159 Epoch 458/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2246 - mean_squared_error: 0.2054 Epoch 459/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2219 - mean_squared_error: 0.2027 Epoch 460/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2221 - mean_squared_error: 0.2030 Epoch 461/1000 24/24 [==============================] - ETA: 0s - loss: 0.1894 - mean_squared_error: 0.16 - 0s 540us/step - loss: 0.2302 - mean_squared_error: 0.2111 Epoch 462/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2252 - mean_squared_error: 0.2061 Epoch 463/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2296 - mean_squared_error: 0.2103 Epoch 464/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2302 - mean_squared_error: 0.2111 Epoch 465/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2277 - mean_squared_error: 0.2086 Epoch 466/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2274 - mean_squared_error: 0.2083 Epoch 467/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2261 - mean_squared_error: 0.2070 Epoch 468/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2336 - mean_squared_error: 0.2146 Epoch 469/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2279 - mean_squared_error: 0.2088 Epoch 470/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2375 - mean_squared_error: 0.2183 Epoch 471/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2274 - mean_squared_error: 0.2083 Epoch 472/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2275 - mean_squared_error: 0.2082 Epoch 473/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2295 - mean_squared_error: 0.2104 Epoch 474/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2284 - mean_squared_error: 0.2093 Epoch 475/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2463 - mean_squared_error: 0.2271 Epoch 476/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2323 - mean_squared_error: 0.2131 Epoch 477/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2212 - mean_squared_error: 0.2021 Epoch 478/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2362 - mean_squared_error: 0.2170 Epoch 479/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2143 - mean_squared_error: 0.1950 Epoch 480/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2238 - mean_squared_error: 0.2045 Epoch 481/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2207 - mean_squared_error: 0.2016 Epoch 482/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2267 - mean_squared_error: 0.2076 Epoch 483/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2256 - mean_squared_error: 0.2065 Epoch 484/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2265 - mean_squared_error: 0.2073 Epoch 485/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2334 - mean_squared_error: 0.2144 Epoch 486/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2295 - mean_squared_error: 0.2104 Epoch 487/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2476 - mean_squared_error: 0.2285 Epoch 488/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2331 - mean_squared_error: 0.2141 Epoch 489/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2314 - mean_squared_error: 0.2124 Epoch 490/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2331 - mean_squared_error: 0.2142 Epoch 491/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2299 - mean_squared_error: 0.2110 Epoch 492/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2445 - mean_squared_error: 0.2257 Epoch 493/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2318 - mean_squared_error: 0.2130 Epoch 494/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2300 - mean_squared_error: 0.2111 Epoch 495/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2381 - mean_squared_error: 0.2193 Epoch 496/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2273 - mean_squared_error: 0.2087 Epoch 497/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2677 - mean_squared_error: 0.2490 Epoch 498/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2272 - mean_squared_error: 0.2085 Epoch 499/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2257 - mean_squared_error: 0.2069 Epoch 500/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2207 - mean_squared_error: 0.2020 Epoch 501/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2345 - mean_squared_error: 0.2158 Epoch 502/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2343 - mean_squared_error: 0.2156 Epoch 503/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2262 - mean_squared_error: 0.2076 Epoch 504/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2351 - mean_squared_error: 0.2164 Epoch 505/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2263 - mean_squared_error: 0.2075 Epoch 506/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2409 - mean_squared_error: 0.2221 Epoch 507/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2245 - mean_squared_error: 0.2059 Epoch 508/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2247 - mean_squared_error: 0.2060 Epoch 509/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2384 - mean_squared_error: 0.2198 Epoch 510/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2297 - mean_squared_error: 0.2112 Epoch 511/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2162 - mean_squared_error: 0.1977 Epoch 512/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2281 - mean_squared_error: 0.2094 Epoch 513/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2334 - mean_squared_error: 0.2148 Epoch 514/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2312 - mean_squared_error: 0.2125 Epoch 515/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2204 - mean_squared_error: 0.2018 Epoch 516/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2322 - mean_squared_error: 0.2135 Epoch 517/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2233 - mean_squared_error: 0.2046 Epoch 518/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2419 - mean_squared_error: 0.2232 Epoch 519/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2202 Epoch 520/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2318 - mean_squared_error: 0.2134 Epoch 521/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2273 - mean_squared_error: 0.2089 Epoch 522/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2240 - mean_squared_error: 0.2054 Epoch 523/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2290 - mean_squared_error: 0.2105 Epoch 524/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2281 - mean_squared_error: 0.2095 Epoch 525/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2384 - mean_squared_error: 0.2197 Epoch 526/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2397 - mean_squared_error: 0.2213 Epoch 527/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2371 - mean_squared_error: 0.2185 Epoch 528/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2185 - mean_squared_error: 0.1999 Epoch 529/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2182 - mean_squared_error: 0.1997 Epoch 530/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2154 - mean_squared_error: 0.1967 Epoch 531/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2388 - mean_squared_error: 0.2202 Epoch 532/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2296 - mean_squared_error: 0.2110 Epoch 533/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2322 - mean_squared_error: 0.2136 Epoch 534/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2301 - mean_squared_error: 0.2115 Epoch 535/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2221 - mean_squared_error: 0.2036 Epoch 536/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2149 - mean_squared_error: 0.1962 Epoch 537/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2313 - mean_squared_error: 0.2129 Epoch 538/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2271 - mean_squared_error: 0.2086 Epoch 539/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2212 - mean_squared_error: 0.2026 Epoch 540/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2411 - mean_squared_error: 0.2226 Epoch 541/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2318 - mean_squared_error: 0.2133 Epoch 542/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2403 - mean_squared_error: 0.2218 Epoch 543/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2180 - mean_squared_error: 0.1994 Epoch 544/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2263 - mean_squared_error: 0.2076 Epoch 545/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2325 - mean_squared_error: 0.2139 Epoch 546/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2323 - mean_squared_error: 0.2137 Epoch 547/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2269 - mean_squared_error: 0.2083 Epoch 548/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2335 - mean_squared_error: 0.2149 Epoch 549/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2271 - mean_squared_error: 0.2084 Epoch 550/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2271 - mean_squared_error: 0.2084 Epoch 551/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2236 - mean_squared_error: 0.2048 Epoch 552/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2422 - mean_squared_error: 0.2236 Epoch 553/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2236 - mean_squared_error: 0.2051 Epoch 554/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2475 - mean_squared_error: 0.2291 Epoch 555/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2410 - mean_squared_error: 0.2225 Epoch 556/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2287 - mean_squared_error: 0.2103 Epoch 557/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2350 - mean_squared_error: 0.2167 Epoch 558/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2231 - mean_squared_error: 0.2046 Epoch 559/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2292 - mean_squared_error: 0.2109 Epoch 560/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2418 - mean_squared_error: 0.2235 Epoch 561/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2486 - mean_squared_error: 0.2306 Epoch 562/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2547 - mean_squared_error: 0.2367 Epoch 563/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2371 - mean_squared_error: 0.2192 Epoch 564/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2208 - mean_squared_error: 0.2029 Epoch 565/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2134 - mean_squared_error: 0.1953 Epoch 566/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2387 - mean_squared_error: 0.2206 Epoch 567/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2263 - mean_squared_error: 0.2083 Epoch 568/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2250 - mean_squared_error: 0.2070 Epoch 569/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2435 - mean_squared_error: 0.2253 Epoch 570/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2373 - mean_squared_error: 0.2194 Epoch 571/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2231 - mean_squared_error: 0.2053 Epoch 572/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2318 - mean_squared_error: 0.2140 Epoch 573/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2300 - mean_squared_error: 0.2121 Epoch 574/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2316 - mean_squared_error: 0.2138 Epoch 575/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2283 - mean_squared_error: 0.2105 Epoch 576/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2278 - mean_squared_error: 0.2099 Epoch 577/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2309 - mean_squared_error: 0.2129 Epoch 578/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2333 - mean_squared_error: 0.2153 Epoch 579/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2378 - mean_squared_error: 0.2198 Epoch 580/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2327 - mean_squared_error: 0.2146 Epoch 581/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2387 - mean_squared_error: 0.2206 Epoch 582/1000 24/24 [==============================] - 0s 538us/step - loss: 0.2376 - mean_squared_error: 0.2195 Epoch 583/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2505 - mean_squared_error: 0.2325 Epoch 584/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2382 - mean_squared_error: 0.2202 Epoch 585/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2318 - mean_squared_error: 0.2139 Epoch 586/1000 24/24 [==============================] - ETA: 0s - loss: 0.2190 - mean_squared_error: 0.20 - 0s 665us/step - loss: 0.2408 - mean_squared_error: 0.2228 Epoch 587/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2248 - mean_squared_error: 0.2069 Epoch 588/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2340 - mean_squared_error: 0.2162 Epoch 589/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2276 - mean_squared_error: 0.2098 Epoch 590/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2220 - mean_squared_error: 0.2041 Epoch 591/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2303 - mean_squared_error: 0.2124 Epoch 592/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2423 - mean_squared_error: 0.2244 Epoch 593/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2537 - mean_squared_error: 0.2361 Epoch 594/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2263 - mean_squared_error: 0.2085 Epoch 595/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2292 - mean_squared_error: 0.2114 Epoch 596/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2239 - mean_squared_error: 0.2059 Epoch 597/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2195 - mean_squared_error: 0.2016 Epoch 598/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2237 - mean_squared_error: 0.2057 Epoch 599/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2415 - mean_squared_error: 0.2235 Epoch 600/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2457 - mean_squared_error: 0.2280 Epoch 601/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2381 - mean_squared_error: 0.2203 Epoch 602/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2422 - mean_squared_error: 0.2244 Epoch 603/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2202 - mean_squared_error: 0.2024 Epoch 604/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2297 - mean_squared_error: 0.2120 Epoch 605/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2347 - mean_squared_error: 0.2168 Epoch 606/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2194 - mean_squared_error: 0.2016 Epoch 607/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2454 - mean_squared_error: 0.2274 Epoch 608/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2311 - mean_squared_error: 0.2133 Epoch 609/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2153 - mean_squared_error: 0.1974 Epoch 610/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2293 - mean_squared_error: 0.2112 Epoch 611/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2355 - mean_squared_error: 0.2175 Epoch 612/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2282 - mean_squared_error: 0.2102 Epoch 613/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2169 - mean_squared_error: 0.1988 Epoch 614/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2278 - mean_squared_error: 0.2098 Epoch 615/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2342 - mean_squared_error: 0.2161 Epoch 616/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2306 - mean_squared_error: 0.2127 Epoch 617/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2188 - mean_squared_error: 0.2008 Epoch 618/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2264 - mean_squared_error: 0.2086 Epoch 619/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2323 - mean_squared_error: 0.2145 Epoch 620/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2377 - mean_squared_error: 0.2198 Epoch 621/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2349 - mean_squared_error: 0.2169 Epoch 622/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2332 - mean_squared_error: 0.2152 Epoch 623/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2374 - mean_squared_error: 0.2195 Epoch 624/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2398 - mean_squared_error: 0.2219 Epoch 625/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2315 - mean_squared_error: 0.2137 Epoch 626/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2372 - mean_squared_error: 0.2194 Epoch 627/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2200 - mean_squared_error: 0.2022 Epoch 628/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2178 - mean_squared_error: 0.1999 Epoch 629/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2187 - mean_squared_error: 0.2007 Epoch 630/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2312 - mean_squared_error: 0.2133 Epoch 631/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2294 - mean_squared_error: 0.2115 Epoch 632/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2259 - mean_squared_error: 0.2080 Epoch 633/1000 24/24 [==============================] - 0s 831us/step - loss: 0.2333 - mean_squared_error: 0.2154 Epoch 634/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2328 - mean_squared_error: 0.2150 Epoch 635/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2394 - mean_squared_error: 0.2215 Epoch 636/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2468 - mean_squared_error: 0.2290 Epoch 637/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2263 - mean_squared_error: 0.2087 Epoch 638/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2436 - mean_squared_error: 0.2258 Epoch 639/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2319 - mean_squared_error: 0.2143 Epoch 640/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2476 - mean_squared_error: 0.2299 Epoch 641/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2280 - mean_squared_error: 0.2103 Epoch 642/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2381 - mean_squared_error: 0.2205 Epoch 643/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2358 - mean_squared_error: 0.2181 Epoch 644/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2319 - mean_squared_error: 0.2143 Epoch 645/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2243 - mean_squared_error: 0.2067 Epoch 646/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2337 - mean_squared_error: 0.2161 Epoch 647/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2251 - mean_squared_error: 0.2075 Epoch 648/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2298 - mean_squared_error: 0.2122 Epoch 649/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2319 - mean_squared_error: 0.2143 Epoch 650/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2295 - mean_squared_error: 0.2120 Epoch 651/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2144 - mean_squared_error: 0.1968 Epoch 652/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2249 - mean_squared_error: 0.2073 Epoch 653/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2198 - mean_squared_error: 0.2022 Epoch 654/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2528 - mean_squared_error: 0.2353 Epoch 655/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2189 - mean_squared_error: 0.2014 Epoch 656/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2328 - mean_squared_error: 0.2153 Epoch 657/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2306 - mean_squared_error: 0.2131 Epoch 658/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2294 - mean_squared_error: 0.2117 Epoch 659/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2446 - mean_squared_error: 0.2270 Epoch 660/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2252 - mean_squared_error: 0.2078 Epoch 661/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2398 - mean_squared_error: 0.2224 Epoch 662/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2189 - mean_squared_error: 0.2012 Epoch 663/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2253 - mean_squared_error: 0.2079 Epoch 664/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2287 - mean_squared_error: 0.2110 Epoch 665/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2360 - mean_squared_error: 0.2184 Epoch 666/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2332 - mean_squared_error: 0.2156 Epoch 667/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2272 - mean_squared_error: 0.2095 Epoch 668/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2177 - mean_squared_error: 0.2000 Epoch 669/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2267 - mean_squared_error: 0.2090 Epoch 670/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2161 - mean_squared_error: 0.1985 Epoch 671/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2256 - mean_squared_error: 0.2080 Epoch 672/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2151 - mean_squared_error: 0.1975 Epoch 673/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2282 - mean_squared_error: 0.2106 Epoch 674/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2128 - mean_squared_error: 0.1951 Epoch 675/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2228 - mean_squared_error: 0.2051 Epoch 676/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2204 - mean_squared_error: 0.2029 Epoch 677/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2212 - mean_squared_error: 0.2034 Epoch 678/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2247 - mean_squared_error: 0.2070 Epoch 679/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2260 - mean_squared_error: 0.2083 Epoch 680/1000 24/24 [==============================] - 0s 503us/step - loss: 0.2400 - mean_squared_error: 0.2224 Epoch 681/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2295 - mean_squared_error: 0.2120 Epoch 682/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2311 - mean_squared_error: 0.2134 Epoch 683/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2232 - mean_squared_error: 0.2056 Epoch 684/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2262 - mean_squared_error: 0.2085 Epoch 685/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2204 - mean_squared_error: 0.2028 Epoch 686/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2328 - mean_squared_error: 0.2150 Epoch 687/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2379 - mean_squared_error: 0.2203 Epoch 688/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2240 - mean_squared_error: 0.2065 Epoch 689/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2316 - mean_squared_error: 0.2141 Epoch 690/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2368 - mean_squared_error: 0.2191 Epoch 691/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2299 - mean_squared_error: 0.2122 Epoch 692/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2240 - mean_squared_error: 0.2064 Epoch 693/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2301 - mean_squared_error: 0.2125 Epoch 694/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2391 - mean_squared_error: 0.2216 Epoch 695/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2665 - mean_squared_error: 0.2490 Epoch 696/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2233 - mean_squared_error: 0.2058 Epoch 697/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2370 - mean_squared_error: 0.2194 Epoch 698/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2245 - mean_squared_error: 0.2071 Epoch 699/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2406 - mean_squared_error: 0.2233 Epoch 700/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2391 - mean_squared_error: 0.2217 Epoch 701/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2310 - mean_squared_error: 0.2137 Epoch 702/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2292 - mean_squared_error: 0.2120 Epoch 703/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2323 - mean_squared_error: 0.2150 Epoch 704/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2437 - mean_squared_error: 0.2265 Epoch 705/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2370 - mean_squared_error: 0.2199 Epoch 706/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2264 - mean_squared_error: 0.2093 Epoch 707/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2268 - mean_squared_error: 0.2098 Epoch 708/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2370 - mean_squared_error: 0.2199 Epoch 709/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2311 - mean_squared_error: 0.2141 Epoch 710/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2167 - mean_squared_error: 0.1996 Epoch 711/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2204 - mean_squared_error: 0.2033 Epoch 712/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2206 - mean_squared_error: 0.2033 Epoch 713/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2207 - mean_squared_error: 0.2035 Epoch 714/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2229 - mean_squared_error: 0.2057 Epoch 715/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2224 - mean_squared_error: 0.2052 Epoch 716/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2355 - mean_squared_error: 0.2182 Epoch 717/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2230 - mean_squared_error: 0.2056 Epoch 718/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2295 - mean_squared_error: 0.2122 Epoch 719/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2261 - mean_squared_error: 0.2088 Epoch 720/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2229 - mean_squared_error: 0.2055 Epoch 721/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2302 - mean_squared_error: 0.2128 Epoch 722/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2185 - mean_squared_error: 0.2011 Epoch 723/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2269 - mean_squared_error: 0.2094 Epoch 724/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2195 - mean_squared_error: 0.2021 Epoch 725/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2394 - mean_squared_error: 0.2221 Epoch 726/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2161 - mean_squared_error: 0.1987 Epoch 727/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2360 - mean_squared_error: 0.2187 Epoch 728/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2199 - mean_squared_error: 0.2025 Epoch 729/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2213 - mean_squared_error: 0.2039 Epoch 730/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2516 - mean_squared_error: 0.2343 Epoch 731/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2306 - mean_squared_error: 0.2135 Epoch 732/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2646 - mean_squared_error: 0.2474 Epoch 733/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2321 - mean_squared_error: 0.2151 Epoch 734/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2214 - mean_squared_error: 0.2043 Epoch 735/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2323 - mean_squared_error: 0.2152 Epoch 736/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2217 Epoch 737/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2333 - mean_squared_error: 0.2163 Epoch 738/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2276 - mean_squared_error: 0.2107 Epoch 739/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2519 - mean_squared_error: 0.2350 Epoch 740/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2132 - mean_squared_error: 0.1962 Epoch 741/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2544 - mean_squared_error: 0.2375 Epoch 742/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2368 - mean_squared_error: 0.2199 Epoch 743/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2367 - mean_squared_error: 0.2199 Epoch 744/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2144 - mean_squared_error: 0.1974 Epoch 745/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2169 - mean_squared_error: 0.1998 Epoch 746/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2405 - mean_squared_error: 0.2234 Epoch 747/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2471 - mean_squared_error: 0.2302 Epoch 748/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2378 - mean_squared_error: 0.2207 Epoch 749/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2346 - mean_squared_error: 0.2175 Epoch 750/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2341 - mean_squared_error: 0.2171 Epoch 751/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2279 - mean_squared_error: 0.2109 Epoch 752/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2325 - mean_squared_error: 0.2154 Epoch 753/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2336 - mean_squared_error: 0.2167 Epoch 754/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2222 - mean_squared_error: 0.2053 Epoch 755/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2402 - mean_squared_error: 0.2235 Epoch 756/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2241 - mean_squared_error: 0.2073 Epoch 757/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2342 - mean_squared_error: 0.2174 Epoch 758/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2180 - mean_squared_error: 0.2011 Epoch 759/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2133 - mean_squared_error: 0.1964 Epoch 760/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2226 - mean_squared_error: 0.2058 Epoch 761/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2250 - mean_squared_error: 0.2082 Epoch 762/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2388 - mean_squared_error: 0.2219 Epoch 763/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2230 - mean_squared_error: 0.2061 Epoch 764/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2302 - mean_squared_error: 0.2133 Epoch 765/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2382 - mean_squared_error: 0.2214 Epoch 766/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2216 - mean_squared_error: 0.2047 Epoch 767/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2353 - mean_squared_error: 0.2183 Epoch 768/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2330 - mean_squared_error: 0.2162 Epoch 769/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2437 - mean_squared_error: 0.2269 Epoch 770/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2250 - mean_squared_error: 0.2082 Epoch 771/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2301 - mean_squared_error: 0.2133 Epoch 772/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2300 - mean_squared_error: 0.2133 Epoch 773/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2301 - mean_squared_error: 0.2134 Epoch 774/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2462 - mean_squared_error: 0.2296 Epoch 775/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2352 - mean_squared_error: 0.2185 Epoch 776/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2549 - mean_squared_error: 0.2383 Epoch 777/1000 24/24 [==============================] - 0s 706us/step - loss: 0.2281 - mean_squared_error: 0.2114 Epoch 778/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2247 - mean_squared_error: 0.2080 Epoch 779/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2252 - mean_squared_error: 0.2085 Epoch 780/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2172 - mean_squared_error: 0.2004 Epoch 781/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2191 - mean_squared_error: 0.2024 Epoch 782/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2329 - mean_squared_error: 0.2162 Epoch 783/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2422 - mean_squared_error: 0.2255 Epoch 784/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2296 - mean_squared_error: 0.2128 Epoch 785/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2301 - mean_squared_error: 0.2135 Epoch 786/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2194 - mean_squared_error: 0.2026 Epoch 787/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2351 - mean_squared_error: 0.2182 Epoch 788/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2282 - mean_squared_error: 0.2111 Epoch 789/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2355 - mean_squared_error: 0.2186 Epoch 790/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2283 - mean_squared_error: 0.2113 Epoch 791/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2246 - mean_squared_error: 0.2078 Epoch 792/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2528 - mean_squared_error: 0.2360 Epoch 793/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2268 - mean_squared_error: 0.2099 Epoch 794/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2320 - mean_squared_error: 0.2149 Epoch 795/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2280 - mean_squared_error: 0.2111 Epoch 796/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2265 - mean_squared_error: 0.2096 Epoch 797/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2559 - mean_squared_error: 0.2390 Epoch 798/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2195 - mean_squared_error: 0.2026 Epoch 799/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2276 - mean_squared_error: 0.2107 Epoch 800/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2287 - mean_squared_error: 0.2118 Epoch 801/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2290 - mean_squared_error: 0.2121 Epoch 802/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2263 - mean_squared_error: 0.2094 Epoch 803/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2251 - mean_squared_error: 0.2082 Epoch 804/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2279 - mean_squared_error: 0.2109 Epoch 805/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2201 - mean_squared_error: 0.2032 Epoch 806/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2368 - mean_squared_error: 0.2199 Epoch 807/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2380 - mean_squared_error: 0.2212 Epoch 808/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2314 - mean_squared_error: 0.2146 Epoch 809/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2374 - mean_squared_error: 0.2207 Epoch 810/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2314 - mean_squared_error: 0.2146 Epoch 811/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2299 - mean_squared_error: 0.2132 Epoch 812/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2297 - mean_squared_error: 0.2130 Epoch 813/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2240 - mean_squared_error: 0.2072 Epoch 814/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2273 - mean_squared_error: 0.2106 Epoch 815/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2334 - mean_squared_error: 0.2165 Epoch 816/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2294 - mean_squared_error: 0.2126 Epoch 817/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2224 - mean_squared_error: 0.2055 Epoch 818/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2291 - mean_squared_error: 0.2123 Epoch 819/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2166 - mean_squared_error: 0.1996 Epoch 820/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2154 - mean_squared_error: 0.1984 Epoch 821/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2280 - mean_squared_error: 0.2111 Epoch 822/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2402 - mean_squared_error: 0.2233 Epoch 823/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2610 - mean_squared_error: 0.2442 Epoch 824/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2476 - mean_squared_error: 0.2308 Epoch 825/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2252 - mean_squared_error: 0.2085 Epoch 826/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2234 - mean_squared_error: 0.2066 Epoch 827/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2444 - mean_squared_error: 0.2276 Epoch 828/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2328 - mean_squared_error: 0.2160 Epoch 829/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2242 - mean_squared_error: 0.2075 Epoch 830/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2266 - mean_squared_error: 0.2098 Epoch 831/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2347 - mean_squared_error: 0.2179 Epoch 832/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2221 Epoch 833/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2234 - mean_squared_error: 0.2067 Epoch 834/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2328 - mean_squared_error: 0.2163 Epoch 835/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2244 - mean_squared_error: 0.2078 Epoch 836/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2220 - mean_squared_error: 0.2053 Epoch 837/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2153 - mean_squared_error: 0.1986 Epoch 838/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2465 - mean_squared_error: 0.2298 Epoch 839/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2249 - mean_squared_error: 0.2083 Epoch 840/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2280 - mean_squared_error: 0.2114 Epoch 841/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2470 - mean_squared_error: 0.2305 Epoch 842/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2203 - mean_squared_error: 0.2036 Epoch 843/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2342 - mean_squared_error: 0.2177 Epoch 844/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2220 - mean_squared_error: 0.2054 Epoch 845/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2256 - mean_squared_error: 0.2091 Epoch 846/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2375 - mean_squared_error: 0.2210 Epoch 847/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2242 - mean_squared_error: 0.2076 Epoch 848/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2217 - mean_squared_error: 0.2052 Epoch 849/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2341 - mean_squared_error: 0.2178 Epoch 850/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2275 - mean_squared_error: 0.2110 Epoch 851/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2255 - mean_squared_error: 0.2089 Epoch 852/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2244 - mean_squared_error: 0.2080 Epoch 853/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2286 - mean_squared_error: 0.2120 Epoch 854/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2138 - mean_squared_error: 0.1972 Epoch 855/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2262 - mean_squared_error: 0.2095 Epoch 856/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2290 - mean_squared_error: 0.2123 Epoch 857/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2242 - mean_squared_error: 0.2076 Epoch 858/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2314 - mean_squared_error: 0.2148 Epoch 859/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2331 - mean_squared_error: 0.2165 Epoch 860/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2234 - mean_squared_error: 0.2068 Epoch 861/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2377 - mean_squared_error: 0.2212 Epoch 862/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2356 - mean_squared_error: 0.2190 Epoch 863/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2352 - mean_squared_error: 0.2187 Epoch 864/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2208 - mean_squared_error: 0.2042 Epoch 865/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2202 - mean_squared_error: 0.2036 Epoch 866/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2215 - mean_squared_error: 0.2048 Epoch 867/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2207 - mean_squared_error: 0.2041 Epoch 868/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2385 - mean_squared_error: 0.2219 Epoch 869/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2296 - mean_squared_error: 0.2129 Epoch 870/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2332 - mean_squared_error: 0.2165 Epoch 871/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2255 - mean_squared_error: 0.2088 Epoch 872/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2410 - mean_squared_error: 0.2242 Epoch 873/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2371 - mean_squared_error: 0.2203 Epoch 874/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2391 - mean_squared_error: 0.2224 Epoch 875/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2458 - mean_squared_error: 0.2293 Epoch 876/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2286 - mean_squared_error: 0.2120 Epoch 877/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2188 - mean_squared_error: 0.2020 Epoch 878/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2372 - mean_squared_error: 0.2206 Epoch 879/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2345 - mean_squared_error: 0.2180 Epoch 880/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2299 - mean_squared_error: 0.2133 Epoch 881/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2277 - mean_squared_error: 0.2111 Epoch 882/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2336 - mean_squared_error: 0.2171 Epoch 883/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2151 - mean_squared_error: 0.1986 Epoch 884/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2233 - mean_squared_error: 0.2067 Epoch 885/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2319 - mean_squared_error: 0.2154 Epoch 886/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2235 - mean_squared_error: 0.2069 Epoch 887/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2240 - mean_squared_error: 0.2074 Epoch 888/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2434 - mean_squared_error: 0.2269 Epoch 889/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2265 - mean_squared_error: 0.2100 Epoch 890/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2396 - mean_squared_error: 0.2231 Epoch 891/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2266 - mean_squared_error: 0.2101 Epoch 892/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2250 - mean_squared_error: 0.2086 Epoch 893/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2314 - mean_squared_error: 0.2149 Epoch 894/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2254 - mean_squared_error: 0.2089 Epoch 895/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2302 - mean_squared_error: 0.2136 Epoch 896/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2227 - mean_squared_error: 0.2061 Epoch 897/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2275 - mean_squared_error: 0.2109 Epoch 898/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2222 - mean_squared_error: 0.2056 Epoch 899/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2285 - mean_squared_error: 0.2120 Epoch 900/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2333 - mean_squared_error: 0.2168 Epoch 901/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2276 - mean_squared_error: 0.2111 Epoch 902/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2253 - mean_squared_error: 0.2088 Epoch 903/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2222 - mean_squared_error: 0.2057 Epoch 904/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2260 - mean_squared_error: 0.2095 Epoch 905/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2254 - mean_squared_error: 0.2088 Epoch 906/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2311 - mean_squared_error: 0.2148 Epoch 907/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2476 - mean_squared_error: 0.2312 Epoch 908/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2394 - mean_squared_error: 0.2230 Epoch 909/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2388 - mean_squared_error: 0.2224 Epoch 910/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2264 - mean_squared_error: 0.2098 Epoch 911/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2292 - mean_squared_error: 0.2126 Epoch 912/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2227 - mean_squared_error: 0.2061 Epoch 913/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2302 - mean_squared_error: 0.2135 Epoch 914/1000 24/24 [==============================] - 0s 499us/step - loss: 0.2421 - mean_squared_error: 0.2255 Epoch 915/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2290 - mean_squared_error: 0.2123 Epoch 916/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2260 - mean_squared_error: 0.2092 Epoch 917/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2226 - mean_squared_error: 0.2060 Epoch 918/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2197 - mean_squared_error: 0.2029 Epoch 919/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2379 - mean_squared_error: 0.2212 Epoch 920/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2182 - mean_squared_error: 0.2015 Epoch 921/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2231 - mean_squared_error: 0.2064 Epoch 922/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2361 - mean_squared_error: 0.2195 Epoch 923/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2268 - mean_squared_error: 0.2101 Epoch 924/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2439 - mean_squared_error: 0.2274 Epoch 925/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2290 - mean_squared_error: 0.2124 Epoch 926/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2260 - mean_squared_error: 0.2094 Epoch 927/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2236 - mean_squared_error: 0.2071 Epoch 928/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2438 - mean_squared_error: 0.2274 Epoch 929/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2283 - mean_squared_error: 0.2117 Epoch 930/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2498 - mean_squared_error: 0.2334 Epoch 931/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2182 - mean_squared_error: 0.2019 Epoch 932/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2229 - mean_squared_error: 0.2064 Epoch 933/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2396 - mean_squared_error: 0.2231 Epoch 934/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2242 - mean_squared_error: 0.2077 Epoch 935/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2237 - mean_squared_error: 0.2073 Epoch 936/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2213 - mean_squared_error: 0.2048 Epoch 937/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2119 - mean_squared_error: 0.1954 Epoch 938/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2366 - mean_squared_error: 0.2202 Epoch 939/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2355 - mean_squared_error: 0.2191 Epoch 940/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2524 - mean_squared_error: 0.2360 Epoch 941/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2391 - mean_squared_error: 0.2228 Epoch 942/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2280 - mean_squared_error: 0.2118 Epoch 943/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2657 - mean_squared_error: 0.2495 Epoch 944/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2387 - mean_squared_error: 0.2225 Epoch 945/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2338 - mean_squared_error: 0.2176 Epoch 946/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2239 - mean_squared_error: 0.2075 Epoch 947/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2212 - mean_squared_error: 0.2049 Epoch 948/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2158 - mean_squared_error: 0.1995 Epoch 949/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2242 - mean_squared_error: 0.2079 Epoch 950/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2273 - mean_squared_error: 0.2110 Epoch 951/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2371 - mean_squared_error: 0.2208 Epoch 952/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2231 - mean_squared_error: 0.2070 Epoch 953/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2343 - mean_squared_error: 0.2180 Epoch 954/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2183 - mean_squared_error: 0.2021 Epoch 955/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2250 - mean_squared_error: 0.2086 Epoch 956/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2349 - mean_squared_error: 0.2186 Epoch 957/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2201 - mean_squared_error: 0.2035 Epoch 958/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2244 - mean_squared_error: 0.2079 Epoch 959/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2257 - mean_squared_error: 0.2093 Epoch 960/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2351 - mean_squared_error: 0.2187 Epoch 961/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2224 - mean_squared_error: 0.2060 Epoch 962/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2222 - mean_squared_error: 0.2058 Epoch 963/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2412 - mean_squared_error: 0.2247 Epoch 964/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2346 - mean_squared_error: 0.2180 Epoch 965/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2299 - mean_squared_error: 0.2135 Epoch 966/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2298 - mean_squared_error: 0.2133 Epoch 967/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2355 - mean_squared_error: 0.2190 Epoch 968/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2285 - mean_squared_error: 0.2120 Epoch 969/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2437 - mean_squared_error: 0.2272 Epoch 970/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2226 - mean_squared_error: 0.2061 Epoch 971/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2175 - mean_squared_error: 0.2010 Epoch 972/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2405 - mean_squared_error: 0.2241 Epoch 973/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2267 - mean_squared_error: 0.2103 Epoch 974/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2289 - mean_squared_error: 0.2123 Epoch 975/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2323 - mean_squared_error: 0.2157 Epoch 976/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2195 - mean_squared_error: 0.2029 Epoch 977/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2230 - mean_squared_error: 0.2065 Epoch 978/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2214 - mean_squared_error: 0.2049 Epoch 979/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2264 - mean_squared_error: 0.2099 Epoch 980/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2327 - mean_squared_error: 0.2161 Epoch 981/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2230 - mean_squared_error: 0.2065 Epoch 982/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2228 - mean_squared_error: 0.2063 Epoch 983/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2347 - mean_squared_error: 0.2182 Epoch 984/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2320 - mean_squared_error: 0.2155 Epoch 985/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2253 - mean_squared_error: 0.2088 Epoch 986/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2238 - mean_squared_error: 0.2072 Epoch 987/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2206 - mean_squared_error: 0.2039 Epoch 988/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2168 - mean_squared_error: 0.2001 Epoch 989/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2122 - mean_squared_error: 0.1955 Epoch 990/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2532 - mean_squared_error: 0.2365 Epoch 991/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2286 - mean_squared_error: 0.2118 Epoch 992/1000 24/24 [==============================] - 0s 540us/step - loss: 0.2320 - mean_squared_error: 0.2153 Epoch 993/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2305 - mean_squared_error: 0.2137 Epoch 994/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2207 - mean_squared_error: 0.2039 Epoch 995/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2433 - mean_squared_error: 0.2265 Epoch 996/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2335 - mean_squared_error: 0.2168 Epoch 997/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2419 - mean_squared_error: 0.2252 Epoch 998/1000 24/24 [==============================] - 0s 623us/step - loss: 0.2193 - mean_squared_error: 0.2027 Epoch 999/1000 24/24 [==============================] - 0s 665us/step - loss: 0.2228 - mean_squared_error: 0.2061 Epoch 1000/1000 24/24 [==============================] - 0s 582us/step - loss: 0.2420 - mean_squared_error: 0.2254 Total Time Taken is : -17.969932317733765
history=history_reg_1_test.history
print(history.keys())
fig=plt.figure(figsize=(15,6))
ax=fig.add_subplot(1,2,1)
ax.plot(history["loss"])
ax.set_title("Training loss")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
ax=fig.add_subplot(1,2,2)
ax.plot(history["mean_squared_error"])
ax.set_title("Mean Squred Error")
ax.set_xlabel("Epoch")
ax.tick_params(axis="both",which="major")
plt.show()
dict_keys(['loss', 'mean_squared_error'])
labels=y_train1.astype("category").dtype.categories
y_pred_reg_1_test=model_reg_1_test.predict(X_valid).astype("int64")
###########################################################
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
print("The Accuracy of the model is : ",accuracy_score(y_valid,y_pred_reg_1_test))
plt.figure(figsize=(12,6))
sns.heatmap(confusion_matrix(y_valid,y_pred_reg_1_test),xticklabels=labels,yticklabels=labels,annot=True)
plt.xlabel("Original")
plt.ylabel("Predicted")
plt.show()
The Accuracy of the model is : 0.405
Please Note We can certainly make a lot of imporvements upon what is presented here but it required a lot of experimentation and other options which includes but not restricted to the concept of convolution and a lot of other things. I think it is beyond the scope of the given problem statement and the mark weightage. In future projects advanced options might be explored.
Most of the observations are pretty clear needn't required a good description whether here and there a little statement has been sprinkled out. Most of the explanations are self-explanatory either in the beginning or within the itself. Thanks for the valuable feedback.